copyright 2020, john sena akoto

Deep Seismic Structure of Aleutian Subduction Zone Using Teleseismic PdP and SdS Precursor Functions

by

John Sena Akoto, B.S.

A Thesis

In

Geoscience

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfilment of the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. Harold Gurrola Chair of Committee

Dr. George Asquith

Dr. Dustin Sweet

Mark Sheridan Dean of the Graduate School

December, 2020

Texas Tech University, John Sena Akoto, December 2020

ii

ACKNOWLEDGMENTS

First and foremost, I would like to thank Dr. Harold Gurrola. With his support and guidance, I

was able to successfully complete this thesis. He provided much help with Matlab

programming and with understanding complex geophysical concepts. He pushed me to work

more independently and trust my ability.

Next, I extend my gratitude to Dr. George Asquith and Dr. Dustin Sweet. They offered their

time and assisted in several ways. I would also like to thank Tiffany Wiley and Abdul Hafiz

Issah; their collaboration on aspects of my thesis helped to make it a success. I greatly

acknowledge Hannah Snidman for proofreading this thesis and checking for grammar.

Finally, I could not have completed this thesis without financial support from the Texas Tech

University Geoscience Department.


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TABLE OF CONTENTS

ACKNOWLEDGMENTS ............................................................................................................ ii

ABSTRACT ............................................................................................................................... v

LIST OF FIGURES ................................................................................................................... vi

1 INTRODUCTION................................................................................................................ 1

1.1 Background ................................................................................................................ 1

1.2 Purpose and Objectives of the Study ......................................................................... 2

2 LITERATURE REVIEW ..................................................................................................... 3

2.1 Aleutian-Bering Sea Region ....................................................................................... 3

2.1.1 Aleutian Subduction Zone .................................................................................. 4 2.1.2 Bowers Ridge ..................................................................................................... 5

2.2 Transition Zone .......................................................................................................... 8

2.2.1 Introduction ......................................................................................................... 8 2.2.2 The Mantle transition zone controversy ........................................................... 12 2.2.3 Variations in Discontinuity Depths.................................................................... 12 2.2.4 Transition Zone Water Filter ............................................................................. 14

2.3 PdP and SdS Precursor Function ............................................................................ 15

2.4 Studies Investigating the TZ..................................................................................... 18

2.4.1 Topography of the “410” and “660” km seismic discontinuities in the Izu-Bonin subduction zone [Collier and Helffrich, 1997] ........................................ 18

2.4.2 Seismic Imaging of Transition Zone Discontinuities Suggest Hot Mantle West of Hawaii [Q Cao et al., 2011] ................................................................. 19

3 METHODOLOGY............................................................................................................. 23

3.1 Data Collection and Preparation .............................................................................. 24

3.2 Data Processing ....................................................................................................... 27

3.2.1 Ocean Bottom Multiples Removal (Deoceaning) ............................................. 27 3.2.2 Ray Tracing ...................................................................................................... 28 3.2.3 Beamforming .................................................................................................... 29 3.2.4 Deconvolution and Stacking ............................................................................. 32 3.2.5 Single Iterative Deconvolution.......................................................................... 32 3.2.6 Wavefield Iterative Deconvolution .................................................................... 36 3.2.7 GyPSuM Tomography Models ......................................................................... 39

4 RESULTS ........................................................................................................................ 40

4.1 Preface ..................................................................................................................... 40

4.2 Data Density ............................................................................................................. 40

4.3 PdP Profiles ............................................................................................................. 43

4.4 SdS Profiles ............................................................................................................. 50

4.5 Depth Maps and Velocity Models ............................................................................ 58

4.5.1 410 Discontinuity .............................................................................................. 59 4.5.2 660 Discontinuity .............................................................................................. 63 4.5.3 Transition Zone Isopach ................................................................................... 66

5 DISCUSSION ................................................................................................................... 67


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5.1 Discussion ................................................................................................................ 67

5.1.1 Puddle Zones: Anomalies C and E in the Transition Zone .............................. 73

6 CONCLUSIONS............................................................................................................... 82

6.1 Geological Implications ............................................................................................ 82

REFERENCES ......................................................................................................................... 84

APPENDICES .......................................................................................................................... 88


v

ABSTRACT

The Aleutian subduction zone is a site of active volcanism with back-arc spreading and island

arc formation. The goal of this project was to investigate upper mantle structure across the

Aleutian subduction zone using PdP and SdS functions. The Aleutian subduction zone was

chosen as our study area because of its significant tectonic activity coupled with high data

density PdP/SdS midpoints. In this project, we leveraged the US Transportable Array –a

highly dense seismic survey- that provided us with higher quality seismic data. Seismic

images were made using the Wavefield Iterative Deconvolution (WID) stacking method. We

tested the robustness of our results by comparing them with a 3D GyPSuM Earth Model. The

results of our investigation show that where the Pacific plate passes through the transition

zone, the 410 discontinuity is elevated by up to 20 km, and the 660 discontinuity is depressed

by up to 30 km. We interpreted the variations in the boundaries of the transition zone in terms

of phase changes and Claperyon slope, where the 410 discontinuity (phase change)

represents olivine to wadsleyite and has a positive Claperyon slope, while the 660

discontinuity (phase change) represents ringwoodite to perovskite and ferropericlase, and has

a negative Claperyon slope. Also in the transition zone, the 520 discontinuity, which does not

show up globally in seismic data, is observed in regions close to the cold subducting slab and

we suggest this observation is a result of “mantle chilling effect”, where the cold subducting

Pacific slab cools the mantle near the 520 discontinuity, leading to a sharp 520. We infer that

the Pacific slab pools atop the 660 discontinuity and undergoes dehydration, and the release

of water contributes to the 660 depression observed. Other significant upper mantle features

observed from our results were the presence of a Lithosphere Asthenosphere transition zone

(LATZ), between discontinuities observed between depths of 90 to 220 km, and geophysical

evidence of possible remnants of the source for Bowers Ridge, which we identified as an

Island Arc System.


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LIST OF FIGURES

Figure 2.1 Geologic features present in the Aleutian-Bering Sea Region (Based Wikipedia File: Hawaii hotspot.jpg, which was initially generated by National Geophysical Data Center/USGS as published at http://www.ngdc.noaa.gov/mgg/image/2minrelief.html). .............................. 3

Figure 2.2 Map of the Bering Sea showing the three main rises; Bowers Ridge, Umnak Plateau, and Shirshov Ridge. Profile AB intersects Shirshov Ridge, profile CD intersects Bowers Ridge, and profile EF, Umnak Plateau. These profiles show gravity, magnetic, and seismic character of the geologic feature they cross [Ben-Avraham and Cooper, 1981] ................. 6

Figure 2.3 Formation model 1 of the Bowers Ridge, Umnak Plateau, Shirshov Ridge, and Aleutian Ridge A. late Mesozoic; B. early Tertiary. Prior to the formation of the Aleutian subduction zone, the Shirshov ridge, Bowers ridge, and Umnak plateau were driven by plate motion and collided into the Bering shelf. [Ben-Avraham and Cooper, 1981] .................... 7

Figure 2.4 Formation model 2 of the Bowers Ridge, Umnak Plateau, Shirshov Ridge, and Aleutian Ridge. A. and B. is late Mesozoic; C. early Tertiary. (A) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge in their original location. (B) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge transported by seafloor spreading. (C) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge in their current location. [Ben-Avraham and Cooper, 1981] ..................................................... 8

Figure 2.5 Model showing a cross-section of the Earth with the different layers [Bullen, 1986] ................................................................................................... 9

Figure 2.6 Phase diagrams for pyrolitic mantle composition. (A) Left, P- and S wave speed (and mass density ρ) as a function of depth and pressure in Earth’s mantle. Right: Volume fraction of mantle constituents between 200-800km. The green line depicts 410 phase change; the blue line shows 520 phase change, and the red line represents 660 phase change [Q Cao et al., 2011] [Kennett et al., 1995]. ........................................ 11

Figure 2.7 A.Effects of a cold subducting slab on the 410 and 660 km discontinuities; B.Effects of a hot mantle plume on the 410 and 660 km discontinuities; C Green line shows olivine to wadsleyite phase (positive Claperyon slope), orange ringwoodite to perovskite and ferropericlase (post-spinel with negative Claperyon slope) and blue majorite to magnesium perovskite (positive Claperyon slope). 1 is cold geotherm, 2 is average geotherm, 3 is warm geotherm, 4 is anomalously warm geotherm. D Hot upwellings showing post-garnet phase. E Subducting slab[Q Cao et al., 2011] ................................................................................. 13

Figure 2.8 PdP wave path and its precursor P660P reflected on the underside of the 660km discontinuity. Star and red triangle represent the source and receiver, respectively. The red path indicates the PP wave path, and the blue path indicates the P660P wave. ............................................................. 15

Figure 2.9 Seismic records for two stations in a study by [Deuss, 2009] shown with solid lines compared to synthetics based on the PREM model shown in dashed lines. The 660, 410, and 220km discontinuities are detected as precursors to the SdS. ................................................................................... 16

Figure 2.10 A. Global map showing seismic sources in black and receivers in red. B. Bounce-points are shown as black dots in figure 2.12 B. .............................. 17

Figure 2.11 Map illustrating source from Izu-Bonin and receivers in the United Kingdom and Pacific North-West seismic networks. Great circle paths of the seismic waves are shown in solid lines, and plate boundaries are shown in dashed lines [NUVEL-1, 1990]. ...................................................... 18


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Figure 2.12 Left: (Top) Map of study area showing the geographical distribution of ~170,000 surface mid-points of SdS waves –the darker shaded regions have denser coverage; (bottom) trajectory of underside reflections at the surface (SS) and at mantle discontinuities (SdS); precursor stack showing signal associated with S660S, S410S, and SdS waves [after (27)]. Right: (Top) Distribution of ~4800 sources (red symbols) and ~2250 receivers (blue); (bottom) diagram of SdS, S410S, and S660S trajectory [Q Cao et al., 2011] ........................................................................ 20

Figure 2.13 Cross-section across Hawaii. (A) Seismic image overlaid on tomographically inferred wave-speed variation. (B) Enlarged figure between 370 and 760 km, green dashes are interpreted as 410, blue dashed line as 520, and red dashed line as 660 [Q Cao et al., 2011]. .......... 20

Figure 2.14 (A) Topographic map of 410 and (B) Topographic map of 660. The thick black line is the E-W cross-section in Figure.2.13 (C) Isopach between 410 and 660. (D) Correlation between 410 and 660 [Q Cao et al., 2011] ..... 21

Figure 2.15 Schematic model of transition zone beneath Hawaii. Green, blue, and red lines represent 410, 520, and 660, respectively. Dashed arrows show the movement of hot mantle plume within the transition zone [Q Cao et al., 2011] ............................................................................................. 22

Figure 3.1 Flowchart of methodology, shows a summary of the procedure employed in this study ................................................................................... 24

Figure 3.2 Example of a raw seismic time series before filtering .................................... 26

Figure 3.3 Example of seismic data after filtering ........................................................... 26

Figure 3.4 Example of a travel time curve for PdP and P410P (Blue: PdP, Red: P410P) ........................................................................................................... 28

Figure 3.5 Travel time curve for PdP and P660P (Blue: PdP, Black: P660P) ................ 29

Figure 3.6 Illustration of beamforming overview. Seismic records from the same event that falls within the beaming radius ‘r’ are cross-correlated, time-shifted, and stacked. The violet star represents the seismic event, and the orange circles represent seismic recordings from the event that fall within the specified stacking radius ‘r’. ........................................................... 30

Figure 3.7 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z) prior to beamforming. ..................................................................................... 30

Figure 3.8 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z 16) after a 16° beamforming .......................................................................... 31

Figure 3.9 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z 16) after a 32° beamforming .......................................................................... 31

Figure 3.10 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z 16) after a 360° beamforming ........................................................................ 32

Figure 3.11 (A)Signal function and (B)source function prepared for deconvolution process ........................................................................................................... 34

Figure 3.12 (A)autocorrelation function made from convolving source with itself; (B)cross-correlation function made from convolving the signal with source; (C)SdS receiver function deconvolved from cross-correlation function; (D)synthetic function created by convolving SdS receiver function with source function overlain on original signal function; E, F, G, H, shows the second iteration of deconvolution ............................................ 35


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Figure 3.13 (A) SdS receiver function after 10 iterations; (B) synthetic function after 10 iterations overlain on original signal function ............................................ 36

Figure 4.1 Data density map for PdP. Blue shaded areas depict regions of high density, while reddish areas suggest low data density .................................. 41

Figure 4.2 Data Density map for SdS. Blue shaded areas depict regions of high density, while reddish areas suggest low data density .................................. 42

Figure 4.3 Top: Wiggle plot from common stack longitude 162°E. Bottom: Wiggle plot from individual stack longitude 162°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. (Yellow: peak, Blue: trough) ........................................................... 45

Figure 4.4 Colorshading plot for longitude 162°E. 410 km discontinuity seen as a positive amplitude at ~410 km. ...................................................................... 46

Figure 4.5 Top: Wiggle plot from common stack longitude 173°E. Bottom: Wiggle plot from individual stack longitude 173°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 520 km discontinuity is marginally visible (Yellow: peak, Blue: trough). ........................................................................................................... 47

Figure 4.6 Colorshading plot for longitude 173°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 520 km discontinuity partially visible, becomes stronger after latitude 55˚N ................................................................................................... 48

Figure 4.7 Top: Wiggle plot from common stack longitude 196.5°E. Bottom: Wiggle plot from individual stack longitude 196.5°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N (Yellow: peak, Blue: trough). ............................................... 49

Figure 4.8 Colorshading plot for longitude 196.5°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. ......................................................................................................... 50

Figure 4.9 Top: Wiggle plot from common stack longitude 193°E. Bottom: Wiggle plot from individual stack longitude 193°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km. 520 km discontinuity visible after latitude 55˚N. (Yellow: peak, Blue: trough) ...... 52

Figure 4.10 Colorshading plot for longitude 193°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N. 520 km discontinuity visible after latitude 55˚N. .............................................................................. 53

Figure 4.11 Top: Wiggle plot from common stack longitude 172.5°E. Bottom: Wiggle plot from individual stack longitude 172.5°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km (Yellow: peak, Blue: trough). .......................................................................... 55

Figure 4.12 Colorshading plot for longitude 172.5°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N....................................... 56

Figure 4.13 Top: Wiggle plot from common stack longitude 203°E. Bottom: Wiggle plot from individual stack longitude 203°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km (Yellow: peak, Blue: trough). .......................................................................... 57


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Figure 4.14 Colorshading plot for longitude 203°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N....................................... 58

Figure 4.15 Topographic map of P410P. 410 km discontinuity shallows in the central portions and deepens in the NE and SW portions of the map. ...................... 60

Figure 4.16 P wave velocity model for 410. 410 km discontinuity exhibits high seismic velocity anomalies in the central region between latitude 50-60˚N signifying colder than average temperature mantle material. ............... 61

Figure 4.17 Topographic map of S410S. 410 km discontinuity generally shallows in the central portions and deepens in the NE and SW portions of the map. .... 62

Figure 4.18 S wave velocity model for 410. 410 km discontinuity exhibits high seismic velocity anomalies in the central region between latitude 50-60˚N signifying colder than average temperature mantle material. ............... 63

Figure 4.19 Topographic map S660S. Deep anomalies are observed between latitude 50-55˚N.............................................................................................. 64

Figure 4.20 S wave velocity model for 660. High-velocity anomalies can be seen between latitude 50-60˚N and in the NW corner of the map. High-velocity anomalies suggest colder than average mantle material. ............................. 65

Figure 4.21 P wave velocity model for 660. High-velocity anomalies can be seen between latitude 50-60˚N and in the NW corner of the map. High-velocity anomalies suggest colder than average mantle material. ............................. 65

Figure 4.22 Thickness model for Transition Zone shows thicker than average TZ thickness between latitude 50-55˚N ............................................................... 66

Figure 5.1 PP section across longitude 176˚E showing LAB and P220P. The 410 km discontinuity appears at ~410 km and shows a shallowing beneath 50˚N. ................................................................................................ 68

Figure 5.2 SS section across longitude 172.5˚E showing LAB. The 660 km discontinuity appears at ~660 km .................................................................. 68

Figure 5.3 (I) Topographic map of 410. (II) Velocity model of 410. ASZ (white dashed line) represents Aleutian subduction zone; A is a deepening anomaly that demarcates 410 beneath Bowers Ridge; B is a deepening anomaly that demarcates 410 beneath Alaska .............................................. 72

Figure 5.4 (I) Topographic map of 660. (II) Velocity model of 660. ASZ (white dashed line) represents Aleutian subduction zone; C and E are deepening anomalies that demarcates pooling of high-velocity slab material, colder than average slab material at 660; D demarcates 660 beneath Alaska .............................................................................................. 72

Figure 5.5 Thickness of transition zone. C and E show areas with thicker than average TZ where the subducting slab might be puddling and depressing the 660 significantly ..................................................................... 73

Figure 5.6 Depth map to 660 showing anomaly C and E, and profile lines AA’ (170°E), BB’ (185°E), DD’ (200°E), FF’ (42°N), and GG’ (50°N) ................... 74

Figure 5.7 Profile AA’ showing 520 km discontinuity and 660 km discontinuity deepening beyond latitude 50°N (anomaly E) ............................................... 75

Figure 5.8 Profile DD’ showing 660 deepening beneath latitude 45-50°N (anomaly E). 520 km discontinuity visible after latitude 55˚N. ....................................... 75

Figure 5.9 Profile GG’ showing section across anomaly C and E. 660 deepened to ~700 km. ........................................................................................................ 76

Figure 5.10 Control profile FF’, the 660 is observed at the expected depth of ~660 km. ................................................................................................................. 76


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Figure 5.11 (A) Variation of 660 observed in profile AA’. (B)Enlargement of the dashed box around the 660 in profile AA’. The 660 is deepened by up to ~30km. ........................................................................................................... 77

Figure 5.12 (A) Variation of 660 observed in profile BB’. (B) Enlargement of the dashed box around the 660 in profile BB’. 660 here is relatively flat with a maximum deepening of 10 km. ................................................................... 77

Figure 5.13 (A) Absolute velocity profile for longitude DD’. (B) Enlargement of the dashed box showing 410, 520, and 660. Deepening of the 660 is observed along with a velocity reversal (II) of the 660. The 520 (I) visible after latitude 60°N. (C) Enlargement of the dashed box around the 660 to show ~30 deepening on the 660 km discontinuity ..................................... 78

Figure 5.14 (A) Absolute velocity profile for latitude GG’. (B) Enlargement of the dashed box showing high velocity on top of the 660 beneath anomaly C and E. ............................................................................................................. 79

Figure 5.15 S-Wave velocity perturbation profile AA’. High-velocity anomalies are observed between latitude 50-55°N (anomaly C). The deepening of the 660 is noticed between latitude 50-65°N. ...................................................... 80

Figure 5.16 S-Wave velocity perturbation profile DD’. High-velocity anomalies are observed between latitude 50-60°N (anomaly C). The deepening of the 660 is noticed between latitude 50-60°N. The 520 km discontinuity is visible beyond latitude 60°N. .......................................................................... 80

Figure 5.17 S-Wave velocity perturbation profile GG’. The TZ in profile GG’ (across anomaly C and E) is generally of high velocity suggesting a cold TZ ........... 81

Figure 6.1 Cartoon model of Pacific plate subduction beneath the Aleutian subduction zone across profile HH’. Shows elevated 410, 520, and deepened 660. Convective current and mantle chilling leads to sharp 520. Slab dehydration contributes to ~30km deepening observed on the 660, and possible slab rollback on the 660 accounts for cold anomaly seen beneath the ASZ at lower latitude. ........................................................ 83


1

CHAPTER I

INTRODUCTION

1.1 Background

The Aleutian subduction zone is an approximately 4,000 km long convergent plate boundary

that extends from Russia in the east to Alaska in the west. This plate boundary is where the

oceanic Pacific Plate subducts beneath the North American Plate. Subduction rates vary east

to west from 7.5 cm/year to 5.1 cm/year [Brown et al., 2013]. Additionally, the dehydration of

the subducting slab led to the formation of the Aleutian Island Arc, which is a volcanic arc

north of the subduction zone. The subduction zone is also a site for several earthquakes,

including the Good Friday Earthquake that recorded a magnitude of 9.2 on the Richter scale.

The Aleutian subduction zone falls within the Pacific Region, which is a densely sampled

region for PdP/SdS midpoints. A combination of all these features makes the Aleutian

subduction zone an interesting site for study.

Beneath the Aleutian subduction zone, the subducting Pacific Plate interacts with

discontinuities in the upper mantle transition zone (TZ). The transition zone includes three mid

mantle discontinuities at nominal depths of 660km, 520km, and 410km. The interaction of the

Pacific Plate with the 410, 520, and 660 km discontinuities is especially of great interest since

the response of the mid mantle discontinuities to a subducting slab helps reveal the kind of

phase and chemical changes at these discontinuities [Helffrich, 2000]. The prevailing theory is

that the discontinuities in the TZ are the result of phase changes in the olivine system. If this

is the case, the phase changes associated with the 410 and 520 results in an increase in

density and, as a result, enhances convection. However, the phase change at the 660 results

in a decrease in density for down going material, in which case it inhibits convection. Since

subduction zones are essentially downward convection of a cold slab, it makes a good place

to study the nature of these discontinuities and how they affect mantle convection.

The Aleutian subduction zone lacks a dense network of seismic stations that limits seismic

imaging techniques. PdP and SdS methods image boundaries by underside reflections, so


2

they are ideal for imaging regions with poor station coverage. The Aleutian subduction zone is

in a location surrounded by earthquakes from the richest source region on Earth, the west

Pacific ring of fire, that are recorded by the densest seismic network available, the US

transportable array; as a result, there is abundant data for this project. Using PdP and SdS

seismic phases, we will image the upper mantle transition zone to better understand its

properties by determining how it is affected by the subducting Pacific Plate.

1.2 Purpose and Objectives of the Study

The study aimed to investigate the deep structure of the Aleutian subduction zone using

bounce-point PdP and SdS seismic phases. PdP and SdS bounce-point waves allow us to

study areas with a sparse network of seismic recording stations. Moreover, results from this

research will shed more understanding of the upper mantle TZ and how the descending slab

interacts with it. The specific objectives of this project are:

1. Investigate the TZ beneath the Aleutian subduction zone by mapping the depths of

the 410km, 520km, and 660km discontinuities.

2. Explain the mantle dynamics between the subducting slab and the mantle

discontinuities.

3. Investigate the origin of Bowers Ridge


3

CHAPTER II

LITERATURE REVIEW

2.1 Aleutian-Bering Sea Region

The tectonically active Aleutian-Bering sea region is bounded on the south by the Aleutian

Subduction zone, where the Pacific plate subducts beneath the North American plate. The

Bowers and Shirshov ridge are postulated to be island arcs resulting from plate subduction

[Ben-Avraham and Cooper, 1981; D. W. Scholl, 1975]. Additional geologic structures of

tectonic importance include Bering continental margin, Emperor seamounts, Umnak plateau,

and Kamchatka (Commander), Aleutian, and Bowers Basin [Verzhbitsky et al., 2007].

Figure 2.1 Geologic features present in the Aleutian-Bering Sea Region (Based Wikipedia File: Hawaii hotspot.jpg, which was initially generated by National Geophysical Data Center/USGS as published at http://www.ngdc.noaa.gov/mgg/image/2minrelief.html).

The Shirshov ridge is found on the western part of the Aleutian arc. The Bowers ridge loops

north from the Aleutian ridge and extends about 900 km northwest, terminating at the

Shirshov ridge. The Shirshov ridge extends 750 km southward from the Siberian mainland

and borders against the Aleutian arc. In combination, these three ridges cordon the Bering

Sea region into three basins, namely Bowers, Kamchatka (Commander), and Aleutian basin.

Seismic refraction and reflection investigations showed that the Aleutian basin is by far the

largest of the three basins [D. W. Scholl, 1975]. This basin is underlain by approximately 3 km

of semi consolidated sedimentary rock, with seismic velocities ranging from 2.1 to 2.9 km/s.

http://www.ngdc.noaa.gov/mgg/image/2minrelief.html


4

Beneath this semi consolidated rock is a 1 to 6 km layer of lithified sedimentary (or in part

volcanic) rock characterized by seismic velocities ranging from 3.2 to 4.3 km/s. The Bowers

basin also has similar stratification as the Aleutian basin, the difference being that its

sedimentary fill is slightly thinner and directly overlies a thick rock unit of about 5.8 to 6.2

km/s. Ludwig and others have shown that this thick rock unit makes up most of the internal

bulk of the Aleutian and Bowers ridge [Ludwig, 1971]. The Kamchatka basin, which is also

known as the Commander basin, is underlain by 1 to 2 km of semi consolidated deposits

overlying a lower lithified layer of sedimentary rock characterized by seismic velocities of

about 3 km/s. However, due to sparse data, the stratigraphy of the Kamchatka basin is not

known in as much detail as the Aleutian and Bowers basin.

The Bering Continental Margin is a 1,300 km long broad arc, which extends from Kamchatka

to the tip of the Alaska Peninsula. Topographically, the margin separates the Bering Sea and

the Aleutian Basin. The margin is divided into three geomorphic provinces: (1) the flat outer

Bering shelf; (2) the steep Bering continental slope; (3) the deeper and more gently seaward

sloping continental rise. Lithologically, the margin is comprised of two main structural units:

(1) an acoustic basement, made up of lithified rock, and (2) a 1-1.5 km thick overlying

stratified section of semi consolidated sedimentary rock and unconsolidated sediments. The

stratified layer is further divided into three units; a main layered sequence, a rise unit and a

surface-mantling unit [Scholl et al., 1968]

2.1.1 Aleutian Subduction Zone

The Aleutian subduction zone is a ~2500-mile-long convergence boundary where the Pacific

Plate collides and subducts beneath the North American plate at an average rate of 6.3 cm/yr.

It extends from the Alaska Range to the Kamchatka Peninsula [D. W. Scholl, 1975]. The

Aleutian subduction zone is made up of the Aleutian Trench and the Aleutian arc.

The Aleutian trench is a steep and deep V-shaped depression that forms between two

converging plates as the oceanic slab subducts. The trench extends along the southern

coastline of Alaska and the Aleutian island arcs for 3400 km. The island arc forms from

volcanic eruptions from the dehydration of the subducting slab at ~100 km depth.


5

The Aleutian island arc was formed ~50-55 Mya as a result of the subduction of the Kula plate

beneath the North American Plate prior to the subduction of the Pacific plate [Steven

Holbrook et al., 1999]. The island arc formed in 4 phases: the initial phase, the early phase,

the middle phase, and the final phase. The initial phase of arc development began from the

Late Mesozoic to earliest Tertiary, during which the bulk of the arc formed rapidly by mafic

submarine volcanism and plutonism. In the early phase (Eocene to middle Miocene),

volcanism on the arc declined, and tectonically elevated volcanic terranes were sub-aerially

eroded. The early phase was followed by the middle phase (middle Miocene to middle

Pliocene), a period of plutonization and upliftment, which resulted in more erosion of the arc.

During the final phase (middle Pliocene to present), an extensional rifted arc was overlain by

post-orogenic deposits and crested by a chain of andesitic stratovolcanoes [D. W. Scholl,

1975].

2.1.2 Bowers Ridge

The Bowers Ridge is located in the Aleutian Basin. It is an arcuate feature connected to the

Aleutian ridge on the south and extends to the Shirshov ridge on the north; it spans a total

length of 900 km [Verzhbitsky et al., 2007]. The origin of Bowers Ridge is still heavily debated,

with several different hypotheses being proposed.


6

Figure 2.2 Map of the Bering Sea showing the three main rises; Bowers Ridge, Umnak Plateau, and Shirshov Ridge. Profile AB intersects Shirshov Ridge, profile CD intersects Bowers Ridge, and profile EF, Umnak Plateau. These profiles show gravity, magnetic, and seismic character of the geologic feature they cross [Ben-Avraham and Cooper, 1981]

Some of the hypotheses are that the ridge is an ancient, remnant island arc outgrowth of the

Aleutian Ridge and a microcontinent [Kienle, 1971], [Karig, 1972], [Nur and Ben-Avraham,

1978]. Proponents of the hypothesis that Bowers Ridge is older than the Aleutian ridge also

suggest that it was not formed in-situ. Instead, it came to its present location by a more former

plate, possibly the northwards-moving Kula plate, prior to the formation of the Aleutian ridge.

Several models have been developed to support their claim.

One model proposes that prior to the formation of the Aleutian ridge, the subduction zone was

more northwards around the Bering Sea margin, where the Bowers Ridge and Umnak

Plateau were proto structures on the northward bound subducting Kula plate. In this model,

there was also a long, north-trending transform fault west of the Bowers Ridge. As Kula plate

moved north and subducted beneath a more northerly trench, it resulted in the Bowers Ridge

and Umnak Plateau colliding with the southern edge of the Bering Sea margin. This collision

caused the subduction zone to jump southward to the location of the current Aleutian Ridge. It


7

is claimed that this collision event took place by the late Mesozoic or early Tertiary based on

the positions of the Umnak Plateau and the Bowers Ridge [Ben-Avraham and Cooper, 1981].

Figure 2.3 Formation model 1 of the Bowers Ridge, Umnak Plateau, Shirshov Ridge, and Aleutian Ridge A. late Mesozoic; B. early Tertiary. Prior to the formation of the Aleutian subduction zone, the Shirshov ridge, Bowers ridge, and Umnak plateau were driven by plate motion and collided into the Bering shelf. [Ben-Avraham and Cooper, 1981]

A second model by Ben-Avraham and Cooper, 1981 hypothesizes that the Shirshov Ridge,

Umnak Plateau, and the Bowers Ridge were all parts of a large arc structure located east of

the Kamchatka Peninsula. A north-trending spreading ridge is thought to have also existed at

the same time in the area where the present Bering Sea is located [Hilde et al., 1977]. A

series of east-west trending transform faults separated the three proto structures. The pull

from the subduction zone at the Bering Sea margin causes the spreading ridge to eventually


8

caused the Shirshov Ridge, Bowers Ridge, and Umnak Plateau to collide into the Bering Sea

margin. As a result, just like in the prior model, the subduction zone shifted southerly to its

present location at the Aleutian Ridge. Magnetic investigations by Cooper and others (1976)

support this hypothesis since they observed that the magnetic anomalies became older west

of the subducted spreading ridge.

Figure 2.4 Formation model 2 of the Bowers Ridge, Umnak Plateau, Shirshov Ridge, and Aleutian Ridge. A. and B. is late Mesozoic; C. early Tertiary. (A) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge in their original location. (B) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge transported by seafloor spreading. (C) shows the Bowers ridge, Umnak plateau, and the Shirshov ridge in their current location. [Ben-Avraham and Cooper, 1981]

2.2 Transition Zone

2.2.1 Introduction

The transition zone (TZ) was described by [Bullen, 1986] as a diffuse region of high seismic

wave-speed gradient extending from 410 to 1000 km. In his model, he classified the transition


9

zone as Region C with the lower mantle as Region D (figure 2.5). [Birch, 1952] was the first to

suggest discontinuities were caused by polymorphic phase changes. He hypothesized that

the Repetti discontinuity at ~1,000 km marked the top of the lower mantle and that high

seismic wave-velocity gradients are caused by phase changes.

Figure 2.5 Model showing a cross-section of the Earth with the different layers [Bullen, 1986]

[Gutenberg, 1959] proposed early models of the transition region with high wave-speed

gradients without abrupt discontinuities. It was not until the 1960s that sharp jumps in seismic

velocity were discovered at depths of approximately 400 and 650 km. After the initial

discovery of the transition zone discontinuities, several investigators used a variety of

methods to confirm the existence of the 410 and 660 km discontinuities. These discontinuities

defined the boundaries of the transition zone.

Thermodynamic considerations were used to debate that the transition zone discontinuities

are caused by sharp phase changes of olivine to spinel, and then to post-spinel, rather than

chemical changes. They showed that the 410 km discontinuity has a positive Clapeyron

slope, and the deeper 660 has a negative Clapeyron slope [Anderson, 2007]. This means that


10

a cold subducting slab of the same composition as the surrounding mantle will shift the 410

up, enforcing vertical motion in the subducting plate. On the contrary, the cold subducting slab

would depress the 660 km discontinuity, hindering vertical downward movement until the

plate has warmed up to a denser phase. Alternatively, hot rising magma plumes of similar

composition as the mantle will elevate the 660 and depress the 410 km discontinuity.

The TZ is a layer of Earth’s mantle and is located between the upper mantle and the lower

mantle. Thermodynamic considerations have been used to argue that the 410 and 660 km

discontinuities are phase changes caused by pressure-induced changes of crystal structure in

minerals derived from olivine [Anderson, 2007]. Also, based on thermodynamic equilibrium,

the depths of these discontinuities vary depending on temperature and composition. The

depths will, therefore, vary significantly in locations of subducting lithospheric slabs and hot

mantle plumes due to the significant temperature anomalies associated with these features.

To better understand the phase changes associates with discontinuities of the TZ, laboratory

experiments reproduced the temperatures and pressures corresponding to depths up to

750km were performed on olivine, the most abundant mineral in the mantle. At pressures and

temperatures consistent with the 410 km depth, it was observed that α-olivine transformed

into β-spinel. β -spinel (wadsleyite) then changes into γ-spinel at conditions corresponding to

those associated with depths of about 520 km. Indeed, a discontinuity at 520 km has been

reported; however, studies have shown that velocity contrast is too small and gradational

across this discontinuity to produce observable P520P or S520S phases under nominal

mantle conditions. At 660 km pressure, γ-spinel (ringwoodite) transforms into perovskite and

magnesio-wüstite. Recent studies using multi anvil apparatus have shown that, at 660 km

pressures and significantly higher temperatures of 2100-200℃, there exists a post-garnet

phase change that transforms garnet to bridgmanite that would occur at temperature and

pressures consistent with depths of 720 km [Ishii et al., 2018].

The135 Earth Model (figure 2.6) shows the P and S wave velocities as a function of depth

from 200 km to 800 km. In addition, the volume fraction of the primary mantle constituents


11

and how they vary with respect to depth are shown on the right. We can observe a sharp

increase in both P and S waves at the 410 and 660 km discontinuities. We can also find from

the volume fractions the phase changes that occur through the transition zone. Between

200km to 660 km, olivine comprises approximately 60% of the volume fraction, with phase

transitions from olivine to wadsleyite occurring at 410 km, wadsleyite to ringwoodite at 520

km, and ringwoodite to perovskite and ferropericlase around 660 km. Pyroxene and garnet

make up the remaining 40% [Q Cao et al., 2011].

Even though there appears to be a lot of information about the transition zone, the detailed

structure of this region is still mostly controversial, and it remains a subject of investigation.

Figure 2.6 Phase diagrams for pyrolitic mantle composition. (A) Left, P- and S wave speed (and mass density ρ) as a function of depth and pressure in Earth’s mantle. Right: Volume fraction of mantle constituents between 200-800km. The green line depicts 410 phase change; the blue line shows 520 phase change, and the red line represents 660 phase change [Q Cao et al., 2011] [Kennett et al., 1995].


12

2.2.2 The Mantle transition zone controversy

There is an ongoing debate in geochemical literature about whether the 650 or 1000 km

discontinuity is the interface between the upper and lower mantle and whether there are

chemical changes deeper than the 1000 km depth. Currently, geochemical investigations and

convection simulations assume that the 650 km phase change separates the “depleted

convecting upper mantle” from the “primordial undegassed lower mantle.” Geodynamic

modeling suggests that if the 650 km is not a chemical change, then there can be no deeper

chemical change, which in turn implies that the mantle is chemically homogenous. The

transition zone thus holds the key to whether there is whole-mantle or layered-mantle

convection [Anderson, 2007].

2.2.3 Variations in Discontinuity Depths

Cold subducting slabs have been shown to warp the 410 km discontinuity to shallower depths

by about 8 km per 100 K, and depress the 660 km discontinuity down by about 5 km per 100

K (shown in figure 2.7c) [Bina and Helffrich, 1994]. Conversely, unusually high temperatures

will depress the 410 km and elevate the 660 km discontinuity. As a result, the TZ is expected

to thicken by a maximum of about 13 km per 100 K, where temperatures are cold and thin by

a similar amount where temperatures are high (assuming depth anomalies extend across the

TZ) [Bina and Helffrich, 1994]. Anomalous temperatures vary the depth of the 410 and 660 as

a response to their respective Claperyon slopes. The 410 phase change has a positive

Claperyon slope, and as a result, increasing mantle temperature increases the depth of the

410 km discontinuity. On the other hand, the 660 has a negative Claperyon slope, causing the

660 depth to decrease with increasing temperature (shown in figure2.7 C). Aside from cold

subducting slabs and hot mantle plumes can create lateral variations in mantle temperatures

in excess of 200 K that can cause anti-correlated shallowing and deepening of the TZ

discontinuities that can result in thinning or thickening of the TZ of 20-35 km because of anti-

correlated deflections in both discontinuities [Cordery et al., 1997].


13

Figure 2.7 A.Effects of a cold subducting slab on the 410 and 660 km discontinuities; B.Effects of a hot mantle plume on the 410 and 660 km discontinuities; C Green line shows olivine to wadsleyite phase (positive Claperyon slope), orange ringwoodite to perovskite and ferropericlase (post-spinel with negative Claperyon slope) and blue majorite to magnesium perovskite (positive Claperyon slope). 1 is cold geotherm, 2 is average geotherm, 3 is warm geotherm, 4 is anomalously warm geotherm. D Hot upwellings showing post-garnet phase. E Subducting slab[Q Cao et al., 2011]


14

Variations in mantle chemistry also affect the depths and sharpness of the transition zone

discontinuities. High FeO content decreases the depths of the discontinuities and generally

thickens the TZ [G. R. Foulger, 2004]. In addition, studies have shown that water content

increases the sharpness of the 410 km discontinuity [A Cao and Levander, 2010; Mohamed

et al., 2014].

Global investigations have revealed that the average thickness of the transition zone is ~ 242

km[Flanagan and Shearer, 1998]. For example, researchers discovered the thinnest transition

zone, which is 181 km thick beneath Sumatra. Enigmatically, this region also appears to have

a thick accumulation of cold slabs at the base of the TZ, which is expected to thicken the

transition zone. In the western USA, the thickness of the transition zone varies from 220 to

270 km, which suggests a relief of 20 to 30 km on each discontinuity. However, there is no

corresponding surface geology, topography, or plate tectonics to explain the thickening

[Anderson, 2007]. In conclusion, there is a lot to understand about the variation in the

thickness of the transition zone. The transition zone remains a critical region for investigation

since it can help explain how the mantle convects.

2.2.4 Transition Zone Water Filter

The transition zone is considered a water filter because water-bearing minerals are removed

from wet subducting slabs through a slab dehydration process within the TZ [Bercovici and

Karato, 2003; Richard et al., 2006]. Additionally, studies have shown that the transition zone

(the upper mantle between 410 and 660 km) serves as a water reservoir with water occupying

approximately 0.1wt% of the mantle [Bercovici and Karato, 2003]. The transition zone is able

to store water filtered out from the wet subduction slab because of its composition of high

solubility and diffusivity minerals. For example, wadsleyite and ringwoodite (both transition

zone minerals) exhibit high water solubility of 2.4 weight percentage and 2.7 weight

percentage giving them the ability to store water. Thus, wet slabs that stagnate in the

transition zone typically undergo dehydration by expelling water in dense hydrous magnesium

silicates (DHMS) [Richard et al., 2006]. This process of slab dehydration releases water,

which shallows the 410 and deepens the 660, essentially thickening the transition zone[Bina

and Helffrich, 1994].


15

2.3 PdP and SdS Precursor Function

PP and SS body waves also known as underside bounce-point reflections are from the

midpoint between an earthquake source and a seismic receiver. PdP or SdS phases travel

similar trajectories to the PP and SS phases, but instead of traveling to the surface, they

reflect at a discontinuity at a depth “d” below the surface. PdP and SdS phases are referred to

as precursors because they travel a shorter time than the PP or SS and, as a result, arrive

earlier (figure2.8 and figure2.9). For example, P410P is the precursor that reflects off the

underside of the 410 km discontinuity.

Figure 2.8 PdP wave path and its precursor P660P reflected on the underside of the 660km discontinuity. Star and red triangle represent the source and receiver, respectively. The red path indicates the PP wave path, and the blue path indicates the P660P wave.


16

Figure 2.9 Seismic records for two stations in a study by [Deuss, 2009] shown with solid lines compared to synthetics based on the PREM model shown in dashed lines. The 660, 410, and 220km discontinuities are detected as precursors to the SdS.

PdP and SdS phases are often used to study the mantle of the earth, precisely the existence,

and characteristics of discontinuities within the transition zone (410, 520, and 660

discontinuities). PdP and SdS waves have an advantage over other seismic phases because

they provide significant coverage in both oceanic and continental regions, regardless of the

density of seismic recording networks in those regions [Chambers et al., 2005; Deuss, 2009;

Flanagan and Shearer, 1998; Helffrich, 2000; Lawrence and Shearer, 2006; Ritsema et al.,

2002; Schäfer et al., 2009]. Bounce-point data is ideal for studying regions in the North Pacific

and South Pacific, such as Alaska, Aleutian Trench, Chinook Trough, Hawaii Islands, etc.

where there is a paucity of seismic stations. Owing to the central and northern Pacific being

surrounded by seismically active subduction zones, and very dense seismic networks in East

Asia, and North America (particularly the very dense US transportable array), they are the

best-illuminated region in the world by PdP and SdS phases (figure2.10B).


17

Figure 2.10 A. Global map showing seismic sources in black and receivers in red. B. Bounce-points are shown as black dots in figure 2.12 B.

B

A


18

2.4 Studies Investigating the TZ

2.4.1 Topography of the “410” and “660” km seismic discontinuities in the Izu-Bonin subduction zone [Collier and Helffrich, 1997]

The focus of this investigation was to better understand the effects of a subducting slab on

the “410” and “660” km mantle discontinuities in the Izu-Bonin region in the western Pacific.

The study aimed at showing that these discontinuities are because of thermodynamic phase

transformation rather than chemical compositional change. To prove this, they hypothesized

that, “A compositional boundary should be largely indifferent to a temperature change

whereas the thermodynamic properties of a phase transformation prescribe elevation or

depression of the discontinuity depending on the sign and magnitude of the ‘Clapeyron

slope.’”

To perform their investigation, [Collier and Helffrich, 1997]used topside S-to-P discontinuity

conversions and underside discontinuity reflections collected from networks of vertical

seismometers in the United Kingdom and the northwestern United States. They analyzed

data collected from 21 earthquakes with depths between 40 and 548 km and magnitudes

between 5.4 and 6.4. The combination of array data from the UK and the US allowed

interrogation of the discontinuities from both sides of the slab.

Figure 2.11 Map illustrating source from Izu-Bonin and receivers in the United Kingdom and Pacific North-West seismic networks. Great circle paths of the seismic waves are shown in solid lines, and plate boundaries are shown in dashed lines [NUVEL-1, 1990].

The results of the study showed an anti-correlation between the “440” and “660” expected for

a phase change origin of both discontinuities with Claperyon slopes of opposite signs. They


19

also found that the “410” was elevated by about 60 km, and the “660” was depressed by 40

km in similar regions. They estimated that the temperature of the slab interior was 600±75℃ at

a depth of 350 km. They also tentatively suggested a broader 410 discontinuity in the interior

of the slab.

2.4.2 Seismic Imaging of Transition Zone Discontinuities Suggest Hot Mantle West of Hawaii [Q Cao et al., 2011]

The object of this study was to constrain the thermal plume beneath Hawaii as well as its

effects on the transition zone discontinuities. The research suggests that molten material

does not rise directly from the lower mantle through a narrow vertical plume beneath Hawaii.

Instead, the study proposes that the plume accumulates near the base of the transition zone

before being channeled in a flow toward Hawaii and other islands.

In this study, [Q Cao et al., 2011] uses underside reflections (SdS), which arrive as precursors

to surface-reflected SdS waves at sensors far from the study region. In their investigation,

three-dimensional (3D) inverse scattering of SdS wavefield is combined with a method known

as generalized Radon transform to process the SdS data. This unique processing flow avoids

the problem of low spatial resolution, which typically plagues the conventional method of

stacking specular (mirror-like) SdS reflections across large (10° to 20° wide) geographical

bins. They imaged the transition zone beneath Hawaii with approximately 170,000 broadband

records of the SdS wavefield. They analyzed seismic data recorded by ~2250 seismographic

stations around the Pacific. From ~ 4800 earthquakes with depths greater than 75km and

magnitudes greater than 5.2. They had relatively good data coverage across their study area,

with an exception in the southwest region.


20

Figure 2.12 Left: (Top) Map of study area showing the geographical distribution of ~170,000 surface mid-points of SdS waves –the darker shaded regions have denser coverage; (bottom) trajectory of underside reflections at the surface (SS) and at mantle discontinuities (SdS); precursor stack showing signal associated with S660S, S410S, and SdS waves [after (27)]. Right: (Top) Distribution of ~4800 sources (red symbols) and ~2250 receivers (blue); (bottom) diagram of SdS, S410S, and S660S trajectory [Q Cao et al., 2011]

After processing the data, a cross-section was taken across the 3D image volume. The cross-

section can be seen in the plan view in figure 2.14. From the cross-section, they were able to

track the “410”, “520”, and “660”. The “660” in the region I (below and east of Hawaii) is

slightly shallower than the global average (~650 km). In region II (between Hawaii and 165°

W), the “660” is more anomalous (~640 km). Between 167° and 179°W (region III), the “410”

reaches 430 km, and the “660” appears anomalously deep (~700 km).

Figure 2.13 Cross-section across Hawaii. (A) Seismic image overlaid on tomographically inferred wave-speed variation. (B) Enlarged figure between 370 and 760 km, green dashes are interpreted as 410, blue dashed line as 520, and red dashed line as 660 [Q Cao et al., 2011].


21

In figure 2.14 it is observed that the transition zone is relatively thin beneath Hawaii and thick

on the west of Hawaii (region III). In addition, the correlation between 410 and 660 depth

variations is negatively correlated beneath Hawaii, but conspicuously positive correlated in

region III.

Figure 2.14 (A) Topographic map of 410 and (B) Topographic map of 660. The thick black line is the E-W cross-section in Figure.2.13 (C) Isopach between 410 and 660. (D) Correlation between 410 and 660 [Q Cao et al., 2011]

From their observations, they concluded that the up doming of the “660” beneath region II is

consistent with the post-spinel transition in hot mantle regions. While the deepening of the

“660” beneath region III was enigmatic to the post-spinel transition, it was interpreted as the


22

post-garnet transition caused by a temperature anomaly at that depth. No defined pathways

of flow from the deep anomaly to the Earth’s surface were resolved. However, the study

suggests that Hawaii's volcanism might stem from the temperature anomaly in region III.

Figure 2.15 Schematic model of transition zone beneath Hawaii. Green, blue, and red lines represent 410, 520, and 660, respectively. Dashed arrows show the movement of hot mantle plume within the transition zone [Q Cao et al., 2011]


23

CHAPTER III

METHODOLOGY

The focus of the study was to investigate the crust and mantle beneath the Aleutian

subduction zone by using PdP and SdS data. As we established earlier, the use of PdP and

SdS data are advantageous to the study of the mantle of areas that lack dense networks of

seismic stations such as the Aleutian subduction zone. Discontinuities can be imaged by

leveraging the differences in the arrival times between the PP/SS phase that reflects off the

surface and the PdP /SdS phase that reflects off mantle discontinuities.

The bounce-point data were obtained from the IRIS Data Management Center, a repository of

seismic data. After downloading, the data were screened and prepared for the analyses that

we run.


24

Figure 3.1 Flowchart of methodology, shows a summary of the procedure employed in this study

3.1 Data Collection and Preparation

All data for this study were acquired from the IRIS Data Management Center. For this study,

we downloaded PdP and SdS data collected for earthquakes of magnitude 5.8 and larger

between January 1990 to November 2018 for every broadband seismic station in the DMC

catalog. We hoped this will give sufficient redundancy of data, thus reducing the effect of few

noisy seismic records on our overall dataset. We downloaded data with a great circle of 60-

180° between station and event.


25

The first step in data preparation was sorting; a sorting technique was done to organize the

data by the station. Next, the sorted data were cut to isolate the seismic phases relevant to

our study. This recording contains all the seismic phases P, S, PcP, PcS, and other phases

as well as the PdP and SdS phases. To isolate the P, S, PdP, and SdS phases, time windows

were selected to allow for precursor information down to 1000km (120 seconds before the

first P arrival to 1800 seconds after the first P arrival).

After the data was sorted and cut, the next steps were to detrend, taper, and filter these data.

The purpose of the aforementioned procedures was to enhance the signal to noise ratio of the

seismic recordings. The data were detrended by fitting a line to the time series and removing

the line from the data. For stable deconvolution and cross-correlation, it is crucial for the data

stream to begin and end with zeros, so we apply a linear taper to each end of these data.

Broadband high gain data obtained from IRIS DMC typically have sampling rates of 20, 40, or

50 samples per second (SPS), depending on the local network and the year that the data

were collected. To use these data, we had to resample all the data to a uniform sampling rate.

For the study, we resampled all our seismic recordings to 10 sps. After tapering, the data was

filtered using a phaseless Gaussian filter. An anti-aliasing low pass filtered was applied to

these data to enable us to decimate it to 10 samples per second, thereby saving on disc

space and speed processing. Low pass filtering does not harm these data because there is

generally no teleseismic signal at frequencies above 1 Hz, and so high-frequency noise tends

to be local noise. To remove low-frequency noise, usually related to station instability, a 0.05

Hz high pass filtered was applied.


26

Figure 3.2 Example of a raw seismic time series before filtering

Figure 3.3 Example of seismic data after filtering

The final step before processing was to quality check the data. This was performed in three

stages:

1. Preliminary scanning: a threshold signal to noise ratio of 2 was used to eliminate poor

seismic traces.

2. Detailed automated scanning was performed and based on great circle arc (to

eliminate data from a distance with interfering phases), peak signal to noise ratio, and

standard deviation signal to noise ratios- to discriminate the data into 3 groups (good,

borderline, and bad).


27

3. Detailed visual inspection: For this stage, we visually inspected all borderline traces

and used our judgment to either assign them to the good or bad class. The bad class

was eliminated.

After quality checking, 658000 PdP traces and 444000 SdS traces qualified to be processed

and used in the study.

3.2 Data Processing

3.2.1 Ocean Bottom Multiples Removal (Deoceaning)

Removal of Ocean Bottom Multiples has become a standard procedure in processing PdP

data [Ailiyasi Ainiwaer, 2014; Duncan, 2012; Rogers, 2013]. Throughout PdP data, there is

usually steady positive arrival of a higher frequency horizon, which arrives marginally earlier

than the PdP phase causing a double PdP peak. This horizon is frequently identified in

recordings from offshore stations and has been shown to be a product of the P wave

bouncing at the ocean bottom-seawater contact. This ocean bottom reflection added to the

regular sea-water-air PdP reflection causes double peaks observed in PdP data. Since the

PdP phase is used as the source in the deconvolution process, this double peak will be

carried on to all the precursors extracted from the signal, which is problematic. To avoid the

issue of precursors with double peaks, all PdP data recorded offshore are de-oceaned (ocean

bottom arrivals are removed) prior to deconvolution. Removing the double peak PdP involved,

first, finding all data recorded offshore, followed by deconvolving the double-peaked PdP from

itself and saving this data into the source channel. While this initial deconvolution causes

precursors to have double peaks, the two PdP impulses will collapse to a single PdP pulse.

The double-peaked precursors in the source channel are inconsequential since they will be

tapered off during source function creation [Duncan, 2012]. Ocean Bottom Multiple Removal

is not carried out on SdS data because S waves cannot travel in fluids, and do not exhibit

multiples due to seawater-air contact.


28

3.2.2 Ray Tracing

Raytracing and travel time calculation is a process of computing the travel time and distance

traveled by all possible PdP, SdS, PdP, and SdS phases from the surface to 700 km. The

travel times and depths calculated are interpolated based on a given velocity model. In our

study, we used the 1-D IASPI 91 velocity model[Kennett and Engdahl, 1991] to compute our

travel times and depths. A travel-time calculation program, which was written by [Duncan,

2012], was used to calculate the expected arrival times for PdP and SdS across the

teleseismic range (30° to 180°)

Figure 3.4 Example of a travel time curve for PdP and P410P (Blue: PdP, Red: P410P)


29

Figure 3.5 Travel time curve for PdP and P660P (Blue: PdP, Black: P660P)

3.2.3 Beamforming

The next step in processing the screened data is beamforming. Beamforming is a terminology

that refers to a wide array of geophysical processing techniques. However, in this study,

beamforming simply refers to time-shifting and stacking all seismic data from the same event

that falls within a specified search radius at the receiver end of the data. Stacking data from

the same event at the receiver end does one of two things depending on whether a small or

large stacking radius is chosen. When a small stacking radius is used to beamform, the stack

produced generally maintains its local phases (such as Moho phases) as well as local

lithosphere and mantle variations. This is because a few close traces from the same event

are stacked, resulting in a stack that amplifies local crustal and mantle variations common to

the traces in the radius. However, if a large stacking radius is used, the stack generally

averages out local phases, crustal and mantle variations, resulting in a trace that seems to

have traveled through a generally uniform crust and mantle.

In this study, several stacking radii were selected: 1°, 2°, 4°, 8°, 16°, 32°, and 360°. From

these stacking radii, 360° bin, which encompasses all traces of the same event from the

globe, was used as a clean PdP and SdS source. Signal to noise ratios in this bin was


30

maximized, and phases due to local variations were averaged out. For the signal, we varied

between the 1°, 2°, 4°, and 8° stacking bins. We noticed that the 4° bin consistently gave us

cleaner, high signal to noise ratios without compromising local variations. Thus, we settled on

the 4° bin for our signal.

Figure 3.6 Illustration of beamforming overview. Seismic records from the same event that falls within the beaming radius ‘r’ are cross-correlated, time-shifted, and stacked. The violet star represents the seismic event, and the orange circles represent seismic recordings from the event that fall within the specified stacking radius ‘r’.

Figure 3.7 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z) prior to beamforming.


31

Figure 3.8 Sample seismic recording (FD_4.5_PdP_US.KVTX.00.BHZ.M__at__2018-11-25T16.49.57.000Z 16) after a 16° beamforming



32


3.2.4 Deconvolution and Stacking

Convolution is simply an mathematical operator where a signal or data stream in time, s(t)

gets smeared into a response function, r(t). In the case of earthquake seismology, the signal

or data stream is a teleseismic shock from an earthquake, and the response function is the

discontinuities in the earth encountered by the traveling earthquake waves, technically known

as the earth response function. The result of this convolution of the earthquake signal with the

earth response function is recorded as a time series by the several seismographs deployed

worldwide, which is then studied by seismologists to understand the earth better. The import

of deconvolution in seismology is simply to reverse the process of convolution by extracting

the source mechanism (earthquake wave) from the recorded time series leaving behind the

earth response function, a function, which reveals characteristic seismic properties of various

discontinuities in the subsurface.

3.2.5 Single Iterative Deconvolution

In a single-event iterative deconvolution, one of the channels from the beamed PdP/SdS data,

preferably a large radius channel, is used as the source function. The signal function is

prepared from a second channel from the beamed data, in this case giving preference to


33

smaller radius channels. A taper is applied to the source function to zero out data before and

after the assumed arrival of the PdP/SdS phase. A 10 second and 40-second time margin

before and after the assumed arrival time of the PdP/SdS phase is employed to prevent

cutting the source function. The signal function is also tapered by cutting out data after the

assumed arrival of the PdP/SdS phase (10-second time margin is used for the cut),

essentially cutting out the source function.

After source and signal preparation, the source function is auto-correlated and cross-

correlated with signal function. The maximum peak and its corresponding time are found from

both the auto-correlation and cross-correlation function. Essentially, the time lag between the

maximum peak in the auto-correlation and the maximum peak in the cross-correlation is the

location of a precursor. Also, the amplitude of the precursor is also given by the amplitude of

the maximum peak from the cross-correlation function normalized by the amplitude of the

maximum peak from the auto-correlation function. With the amplitude and time of the

precursor, we have a PdP receiver function. This PdP receiver function is then convolved with

the source function to reverse the process of deconvolution and produce a synthetic

seismogram with the PdP phase that was initially extracted. The synthetic seismogram is

subtracted from the signal function, updating the signal function so that the peak that was

removed prior would no longer be present in the current signal function. The cross-correlation

is repeated, and the next largest peak will be removed and stored in the PdP receiver function

as the second precursor, then convolved with the source and subtracted from the current

signal function. We iterate this process for a fixed number of times where we believe the

signal function is used up and does not contain any more precursor phases.


34

Figure 3.11 (A)Signal function and (B)source function prepared for deconvolution process


35

Figure 3.12 (A)autocorrelation function made from convolving source with itself; (B)cross-correlation function made from convolving the signal with source; (C)SdS receiver function deconvolved from cross-correlation function; (D)synthetic function created by convolving SdS receiver function with source function overlain on original signal function; E, F, G, H, shows the second iteration of deconvolution


36

Figure 3.13 (A) SdS receiver function after 10 iterations; (B) synthetic function after 10 iterations overlain on original signal function

3.2.6 Wavefield Iterative Deconvolution

Wavefield iterative deconvolution (WID) is simply applying the concept of deconvolution

explained above with the addition of a few complexities. Firstly, the process of deconvolution

is applied iteratively on a specific time series data in hopes of obtaining as many relevant

subsurface discontinuities as possible. Secondly, aside from the source function mixed in the

data, there is usually a significant amount of noise imbued into the recorded time series. To

focus the deconvolution process on extracting relevant discontinuities, several time series

data received close to one another are binned together in a common midpoint gather (CMP).

The CMPs are depth converted and then stacked to increased signal content and reduce

noise. The depth stack of the CMPs is then used as a guide to apply the deconvolution

process on all the individual time series data in the given stack bin [A. Ainiwaer and Gurrola,

2018].

Detailed steps for WID explained below for a midpoint:

1. A common midpoint gather (CMP) that contains multiple source-receiver pairs is

developed.

2. For each trace in the CMP, the signal function is selected from a smaller radius

channel from the beamed data.

3. The source function is developed by tapering amplitudes before and after the arrival

of the PP/SS phase.


37

4. The source function is then convolved with itself to produce the auto-correlation

function. The signal function is convolved with the source function to generate the

cross-correlation function. This procedure is applied to all source-receiver pairs in the

CMP.

5. The autocorrelation and cross-correlation function for each source-receiver pair is

depth converted using a 1-D Earth model (IASPI) and stacked.

6. The maximum peak and its corresponding depth are found from both the stacked

auto-correlation and cross-correlation function. The depth lag between the maximum

peak in the auto-correlation and the maximum peak in the cross-correlation gives the

location of a precursor. The amplitude of the precursor is given by the amplitude of

the maximum peak from the cross-correlation function normalized by the amplitude of

the maximum peak from the auto-correlation function.

7. The deconvolved precursor from the stacked, depth converted auto-correlation and

cross-correlation function is stored on the common stack PdP/SdS receiver function.

8. The 1-D Earth model is used to convert the precursor from the common stack

PdP/SdS receiver function from depth to time. This time from the stacked data is used to

establish a time window, which serves as a guide to locate maximum the amplitudes and

times from all the individual cross-correlation functions in the CMP. Deconvolved

precursors from individual signal functions in the CMP are stored unto their corresponding

PdP/SdS receiver functions.

9. The PdP/SdS receiver functions from the individual signals in the CMP are convolved

with the source function to produce synthetics. Synthetics are subtracted from the

original signal functions in the CMP essentially removing the deconvolved phase from

our original data.

10. Phases are simultaneously deconvolved from the stacked cross-correlation function

unto the common stack PdP/SdS receiver function, and individual cross-correlation

functions unto the individual PdP/SdS receiver functions for a given number of

iterations.

11. The individual PdP/SdS receiver functions are then depth converted and stacked.


38

12. Thus for each midpoint we get a common stack PdP/SdS receiver function and an

individual stack PdP/SdS receiver function.

For this project, a program was written to perform WID on common midpoint gathers in the

study area – Aleutian Trench. The area between latitude 34°N-66°N and longitude 160°E-

210°E was delineated for WID, and midpoints between latitudes and longitudes were selected

at 0.5° increment. Each midpoint served as a stacking bin where all samples that fell within

the bin were deconvolved and stacked to produce a PdP/SdS receiver function. Considering

that each intersecting point between a latitude and a longitude gave a midpoint which was

imaged to a 1000 km, a data cube of dimensions 1000 x 101 x 65 was formed (where the 1st,

2nd, and 3rd dimensions represent depth, longitude, and latitude respectively). As mentioned

earlier, each latitude and longitude midpoint served as a stacking bin for CMP gathers used to

generate a PdP/SdS receiver function. To find the optimal size for stacking bins, we

experimented with a 0.5°, 1°, 1.5°, and 2° stacking bin. From this, we observed that a

stacking bin of 1° maximized the resolution of our PdP/SdS receiver functions. This was

expected since the nodes on our 101 x 65 grid increase by 0.5°, and a 1° stacking bin

provides the best overlap between nodes as compared with 0.5° which provides no overlap,

1.5°, and 2° which overlaps too much.

As discussed earlier, WID iteratively removes maximum peaks from the signal-source

correlation function for a specified number of iterations. To obtain an ideal number of

iterations for our study, we trialed a range of iterations specifically, 10, 20, 40, 80, and 100.

From these trials, we observed that 80 iterations consistently produced stacks with strong,

coherent pulses for the 410 and 660 km discontinuities with minimal noise. The cross-

sections produced from 10, 20, and 40 iterations were generally cleaner but regularly left out

significant pulses. On the other hand, 100 iterations resulted in cluttered profiles with several

random pulses that were incoherent across the section. These random pulses were

interpreted as noise that had little geologic relevance. As a result, we settled on 80 iterations

that give an optimal tradeoff.


39

As mentioned earlier, the WID process, which involves deconvolution and stacking, yielded a

3D cube of PdP and SdS data for our study area. With the data in this 3D form, cross-

sections could be studied along any given latitude and longitude within our study area. And

surface maps at any depth of interest area could be efficiently developed and studied.

3.2.7 GyPSuM Tomography Models

To ensure the robustness of our results, we decided to compare maps of the 410 and 660

from our 3D dataset with velocity models from the GyPSuM Earth Model by Nathan Simmons

and colleagues ([Simmons et al., 2010]. The reason for using the 410 and 660 discontinuity

as benchmarks to test the fidelity of our 3D cube is because, the 410 and 660 have been

mapped comprehensively and are widely accepted to appear globally[Q Cao et al., 2011;

Flanagan and Shearer, 1998; Lawrence and Shearer, 2006]. Thus, a strong similarity

between the GyPSum Earth Model and our 3D model validates our methodology.


40

CHAPTER IV

RESULTS

4.1 Preface

To recapitulate, our results were in the form of a 3D cube of depth stacks, which were imaged

using 80 iterations of deconvolution. Profiles along longitudes, data density maps, surface

maps, and tomographs are used to illustrate our results. Profiles are shown using wiggle plots

and color shading. This project focuses on the upper mantle beneath the Aleutian Subduction

Zone (34°N to 66°N and 210°W to 150°W). Therefore, traces in the wiggle plots and color

shading plots are normalized by pulses from the depth range 400-700 km to better illuminate

our primary zone of interest.

Finally, in discussing our results for each discontinuity, we will summarize the results from

PdP imaging first, followed by the SdS, and then images from the GyPSuM velocity model.

This order will be maintained in the display of all results.

4.2 Data Density

From the data density map for PP, it may appear that data coverage is variable from as low

as 8 traces per bin to a high of 888 traces per bin. However, the mean number of traces per

bin is 244, with a standard deviation of 33. This indicates that most bins have between 178 to

310 traces per bin, which is fairly dense coverage compared to most areas.

Additional observations from the map is a data-rich area that falls between latitude 35°N to

65°N and longitude 165°E to 185° E. This data-rich region sufficiently overlaps with portions

of the subduction zone that is favorable to our investigation. Also, between latitude 35°N to

65°N and longitude 205°E to 210° E, there is a paucity of data which limits our resolution in

that area.


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Figure 4.1 Data density map for PdP. Blue shaded areas depict regions of high density, while reddish areas suggest low data density

From the data density map from SS, the most data-rich bin in the SS data has 2911 traces

per bin, but the mean number of traces per bin is 470, with a standard deviation of 275. This

indicates much more variability in the distribution of SS data.

A data-rich region is observed between latitude 50°N to 65°N and longitude 200°E to 210° E

that overlaps with the subduction zone. There are data-poor regions on the 160°E, and 210°E

Longitude


42

edges of the region. These observations are noted to see how they will affect our seismic

profiles and maps.

Figure 4.2 Data Density map for SdS. Blue shaded areas depict regions of high density, while reddish areas suggest low data density

Longitude


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4.3 PdP Profiles

In total, there were 101 PP profiles across our study area, from 160°E to 210°E spaced at

0.5°. We picked the depths to the major TZ discontinuities in these profiles and produced

depth maps to them. We will focus on 3 profiles in discussing our results. In selecting 3

profiles out of the total, we considered two factors, namely image quality, and how

representative they were of the whole data set. Based on image quality and

representativeness, profiles 162°E, 173°E, and 195.6°E were selected. This served as a

preliminary investigation of the data prior to developing maps of different depths. Each profile

is described using two wiggle plots and a color shading plot with the wiggle plots showing

results of the common stack versus the individual stack. In general, the common stack

resulted in finer and cleaner phases and were used to generate the color shading plot. In the

wiggle plots, a yellow peak, which is positive arrival, signifies an increase in velocity with

greater depth, while a blue peak/negative arrival indicates a velocity reversal. Similarly, a

yellowish to red horizon on the color shading plot indicates a rise in velocity, and a greenish to

blue horizon is indicative of decreased velocity.

The wiggle and color shading cross-section along longitude 162°E represents our middle-

level image quality profile. Data coverage along this profile is usually very good except

between latitude 45°N and 55°N, where data density is slightly lower. In both the common

stack and individual stack profiles, there is a strong positive arrival at approximately 100 km,

which we interpret as the top of the Lithosphere Asthenosphere Transition Zone (LATZ)

[Ainsworth et al., 2014]. This horizon exhibits a relatively high degree of depth variability as

shown by that wide range of depths it occurs in the individual stack (43-170 km). Beneath the

100 km HVZ, a low-velocity zone can be detected at approximately 200 km, given that it is a

negative pulse indicating a low-velocity zone (LVZ), and it occurs at the expected depth of

200 km, we interpret this horizon to be the Lithosphere-Asthenosphere Boundary (LAB)

[Ainsworth et al., 2014; Fischer et al., 2010; Kumar and Kawakatsu, 2011; Plomerová et al.,

2002]. The LAB in this image appears to be consistent, along the whole common stack

profile. In the individual stack, the 200 km LVZ is also observable though with significantly

lower resolution. Right below the 200 km LVZ, there are negative arrivals at approximately


44

250 km and 350 km, respectively. These negative arrivals are not as strong and continuous

compared to the 100 km HVZ and 200 km LVZ and disappear completely between latitudes

60°N and 70°N. The next observable arrival is the 410 km discontinuity, in which a velocity

increase indicates the upper boundary of the transition zone. In both the common and

individual stack, the P410P phase is continuous along the whole cross-section. However,

between latitudes 60°N and 65°N, the 410 km discontinuity exhibits a significantly lower

amplitude. In the individual stack, the depths to the 410 varies ~ 380 km to ~440 km. This

variability of the P410P is within the expected range from other global studies [Bina, 2017].

Finally, there is a strong negative peak at 600 km, which is continuous from latitudes 35°N -

55°N, and gradually dissipates from thereon. In general, the color shading plot exhibits similar

features as the wiggle plots, a 100 km HVZ (LAB), 200 km LVZ (LATZ), P410P, and 600 km

LVL.


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Figure 4.3 Top: Wiggle plot from common stack longitude 162°E. Bottom: Wiggle plot from individual stack longitude 162°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. (Yellow: peak, Blue: trough)


46

Figure 4.4 Colorshading plot for longitude 162°E. 410 km discontinuity seen as a positive amplitude at ~410 km.

One of the images with the best resolution falls along the Longitude 173°E (figure 4.5 and

4.6). This section intersects the subduction zone between latitude 50°N-55°N and crosses

data-rich zones, which explains the good quality of cross-sections. Just like in profile 162°E,

both common stack and individual stack profiles, show a 100km HVZ with relatively high

depth variability (in this case between ~45-125 km). The 200 km LVZ is observable and

consistent along most of the profile aside from between latitude 34°-38°, where it fades away.

Beneath the 200 km LVZ, the negative arrivals at around 250 km and 350 km show up again

but with even weaker amplitudes and consistency than in profile 162°E. As expected, the

P410P in this profile is very coherent across the entire cross-section. Additionally, the P410P

shallows significantly beneath the subduction zone -between latitude 45°N and 55°N-, with

the shallowest depth, 386 km, occurring directly beneath the trench at latitude 51°N. The LVL

at 600 km also appears in this profile. The color shading plot shows very clearly an uptick in

the depth of the P410P at latitude 51°N and generally agrees with all the features identified

and described in the wiggle plots.

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Figure 4.5 Top: Wiggle plot from common stack longitude 173°E. Bottom: Wiggle plot from individual stack longitude 173°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 520 km discontinuity is marginally visible (Yellow: peak, Blue: trough).


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Figure 4.6 Colorshading plot for longitude 173°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 520 km discontinuity partially visible, becomes stronger after latitude 55˚N

The wiggle and color shaded plots for the profile along longitude 196.5°E denotes our poor

image quality profile. This profile intersects portions of Alaska in the north between 59°N and

64°N and the subduction zone between 50°N and 55°N. Data coverage along this profile is

decent, with 600-700 traces per bin, between 35-50°N but decreases in data density for

higher latitudes (400-500 traces per bin). Just as in the previous 2 profiles, the cross-section

along 196.5°E has a strong HVZ at ~ 100 km with a high degree of variability and LVZs at 200

km, 250 km, and 350 km. However, this profile has a high-velocity layer slightly below the 200

km low-velocity layer. This high-velocity layer is consistent and continuous between about

latitude 40°-60°N but disappears elsewhere. The P410P is generally continuous between 30°-

50° N, it breaks out, and reappears with significantly lower amplitudes from 55°N to the end of

the profile. In general, the horizons decrease markedly in continuity after 50°N. This is

expected since there is a corresponding drop off in data density up of 50° N. The LVL at 600

km is also present in this section. The color shaded profiles generally confirm significant

features observed in the wiggle plots.

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Figure 4.7 Top: Wiggle plot from common stack longitude 196.5°E. Bottom: Wiggle plot from individual stack longitude 196.5°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N (Yellow: peak, Blue: trough).


50

Figure 4.8 Colorshading plot for longitude 196.5°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N.

4.4 SdS Profiles

Following the discussion of PdP results, 3 out of a total of 101 SS profiles are described in

detail in this section to represent the range in the quality of the profiles. Profiles 193°E,

172.5°E, and 203°E, which represents significantly different portions of the subduction zone,

are selected. A discussion of these profiles serves as a preliminary investigation of the data

prior to developing maps at different depths beneath the study area.

The first SS profile to be discussed is the cross-section along latitude 193°E, and represents

a good image quality cross-section. Profile 193°N crosses Alaska in the North and the

subduction zone between latitude 51° and 54°N. Data coverage along this profile is generally

good and crosses a high data density region between latitude 55° and 65°N. In both the

common stack and individual stack, an LVZ with layering identified between 36 km and 125

km is interpreted as the top of the LATZ [Ainsworth et al., 2014]. Below this LVZ, a high-

velocity zone (HVZ) consistently appears between 110 km and 150 km. Next, there is a semi-

continuous low-velocity arrival at 200 km, which we interpret as the base of the LATZ. This

LVZ exhibits a deepening trend with increasing latitude, starting off with a depth of 206 km at

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32°N and ending at 290 km at 45°N. The LVZ at 200 km disappears at 45°N and reappears

after 55° at a depth of around 220 km. From there upward, the 200 km discontinuity shallows

with increasing latitude to 182km at the north end of the section. In this SS profile along

longitude 193°E, there are no separate LVLs at 250 km or 300 km, as seen in the PP profiles.

The next distinct arrival observed in this cross-section is the S410S, which is the result of a

velocity increase at the upper boundary of the transition zone. In both the common and

individual stacks, the S410S is continuous along the whole profile aside from latitude 53°-

54°N. The depth trend of the S410S from 32°-66°N is most clearly recognized in the color

shaded plot, where the 410 starts off deep around 420km, between latitude 50-55°N, and

then shallows significantly to ~400 km-beneath the subduction zone. This shallowing

continues through to latitude 58° and then begins to drop off to the expected 410 km depths.

A high-velocity layer is observed at ~500km between latitude 55°- 63°N; this arrival is not

continuous and consistent across the whole profile. Finally, the S660S phase appears

between 630 and 690 km. This phase is continuous, consistent and exhibits a substantial

deepening between latitude 45°-55°N -beneath the subduction zone. In the color shaded plot,

the shallowing and deepening features observed on the S410S and S660S, respectively, can

be seen more clearly.


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Figure 4.9 Top: Wiggle plot from common stack longitude 193°E. Bottom: Wiggle plot from individual stack longitude 193°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km. 520 km discontinuity visible after latitude 55˚N. (Yellow: peak, Blue: trough)


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Figure 4.10 Colorshading plot for longitude 193°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N. 520 km discontinuity visible after latitude 55˚N.

Longitude 172.5°E, which represents an example of moderate image quality, is illustrated

(figure 4.11 and 4.12). This section, with a data density 200-300, intersects the subduction

zone between latitude 50°N-55°N. Just like in profile 193°E, both common stack and

individual stack profiles, show an LVZ with a wide range of depth between 30 km and 120 km.

Beneath this LVZ, a high-velocity zone (HVZ) consistently appears between 110 km and 150

km and generally deepens with increasing latitude until the horizon completely disappears

after 60°N. Next, there is a semi-continuous low-velocity arrival at 200 km that exhibits a

deepening trend with increasing latitude. It starts off with a depth of 206 km at 32°N and

deepens to 290 km by latitude 45°N. The 200 km LVZ disappears after 45°N and reappears

after 55° at a depth of around 220 km. From there onward, the 200 km discontinuity shallows

with increasing latitude to 182km by the end of the section. In this SS profile along longitude

193°E, there are no separate LVLs at 250 km or 30 km, as seen in the PP profiles. The next

distinct arrival observed in this cross-section is S410S, a velocity increase that indicates the

transition zone's upper boundary. In both the common and individual stacks, the S410S

reflectors are semi-continuous and require more effort to follow along the profile, especially

north of latitude 50°N. The depth trend of the S410S is more difficult to recognize in profile

172.5°E. But with some effort, it is observed at 410 km and remains mostly flat until after

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S410S

S660S


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latitude 53°N, where it begins to shallow. This shallowing trend continues to a longitude of

56°N, where it becomes impossible to confidently identify the S410S. The 520 km

discontinuity remains unresolved in this profile. Finally, the S660S phase is identified between

630 and 690 km. This phase is continuous, consistent and exhibits deepening between

latitude 45°-55°N -beneath the subduction zone. In the color shaded plot, the 200 km HVZ,

the S410S, and the S660S deepening features are observed. However, there are several

other less consistent and continuous phases that appear in the cross-section. These phases

are all considered to be as a result of noise.


55

Figure 4.11 Top: Wiggle plot from common stack longitude 172.5°E. Bottom: Wiggle plot from individual stack longitude 172.5°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km (Yellow: peak, Blue: trough).


56

Figure 4.12 Colorshading plot for longitude 172.5°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N

The wiggle and color shaded plots for the profile along longitude 203°E illustrates our poor

image quality profile. This profile intersects portions of Alaska in the north between 59°N and

64°N and the subduction zone between 50°N and 55°N. Data coverage along this profile is

poor between 35-50°N but increases significantly for higher latitudes (55°N-65°N). As in the

previous 2 profiles, the cross-section along 203°E has a strong LVZ between 30-120 km.

Data quality at depths greater than 120 km makes it difficult to uniquely identify horizons in

the wiggle plots. However, the color shaded plot reveals the S410S and S660S, even though

it is not continuous and consistent across the whole profile. Also, the similar depth variations

in the S410S and S660S beneath the subduction zone are observed in the color shaded plot.

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Figure 4.13 Top: Wiggle plot from common stack longitude 203°E. Bottom: Wiggle plot from individual stack longitude 203°E. 410 km discontinuity seen as a yellow peak at ~410 km. Shallowing of 410 horizon between latitude 50-55˚N. 660 km discontinuity seen as a yellow peak at ~ 660km (Yellow: peak, Blue: trough).


58

Figure 4.14 Colorshading plot for longitude 203°E. 410 km discontinuity seen as a positive amplitude at ~410 km with an uptick in depth between latitude 50-55˚N. 660 km discontinuity seen as a positive amplitude at ~660 km with a decrease in depth between latitude 50-55˚N

4.5 Depth Maps and Velocity Models

In the remainder of our discussion, we will focus on the discontinuities in the TZ because our

most interesting results of tectonic significance are at these depths. The models used in this

section to describe the topography of interesting horizons were made by picking horizons

across all 101 longitudes and 65 latitudes. To better understand the significance of depth

variations observed in the 410 km discontinuity, we will compare our horizon maps to velocity

models generated from the GyPSum 3D Earth Model [Simmons et al., 2010].

Generally, lower than average P and S wave velocity in the mantle corresponds to a hotter

than average mantle temperature, while higher than average P and S wave velocity

corresponds to colder than average mantle temperatures. A hot mantle typically correlates to

the presence of mantle plume or upward convection, while a cold mantle generally associates

with downward convection or a cold subducting slab. And as discussed extensively in the

literature review, the depths of the 410 and 660 are directly affected by local variations in

temperature, where higher than average temperatures deepens the 410 and shallows the

660, while lower than average temperatures shallows the 410 and deepens the 660. Velocity

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variation observed along these depth horizons are also important to better understand the

depth variations in the TZ boundaries. We expect regions with high velocity to be cold and

areas with low velocity to be warm. If the depth variations correspond well with velocity

variations, we are safe in interpreting the observations as due to temperature variations; if

not, other explanations may be necessary, such as variations in mantle chemistry or

hydration.

4.5.1 410 Discontinuity

The P410P horizon (figure 4.15) has several notable anomalies worth reporting. The P410P

appears to be deeper in the northern regions, relatively shallower in the central parts under

the subduction zone, and then deepens again in the southern portions of the map. Focusing

on specific anomalies, the depth to the 410 km discontinuity beneath the Alaska Subduction

zone (latitude 50°-55° N) is significantly shallower, particularly between longitude 190°-200°E

and 160°-170°E. However, this shallowing trend beneath the subduction zone is disrupted

between longitude 180°E and 190°E, where a deepening of the 410 km discontinuity is

observed. This deep anomaly is relatively local and is confined to 50°-55°N and 180°E and

190°E and trends NW. A second region with deeper than expected depths on the 410

beneath is observed beneath continental Alaska and the region beneath 30°-50°N and 160°-

190°E.

The P wave velocity model for the 410 shows features that correlate strongly with our P410P

model (figure 4.16). Firstly, the model exhibits high P wave velocity in the north and south,

and relatively low P wave velocity in the central regions beneath the subduction zone, which

correlates well with the expected variations in depth that would be anticipated if they are

controlled by temperature variations. More specifically, there is a strong high-velocity anomaly

between 50°-55°N, particularly between 180°-210°E and 160°-170°E, that would be

associated with cold temperatures. The P wave velocity model also shows a relatively high-

velocity anomaly, indicating cold temperatures, between 50°-55°N and 180°-190°E, which

trends NW. This high-velocity (cold) anomaly contradicts the local 410 deepening anomaly

identified in a similar location in our P410P depth map.


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Figure 4.15 Topographic map of P410P. 410 km discontinuity shallows in the central portions and deepens in the NE and SW portions of the map.


61

Figure 4.16 P wave velocity model for 410. 410 km discontinuity exhibits high seismic velocity anomalies in the central region between latitude 50-60˚N signifying colder than average temperature mantle material.

Just as in the P410P horizon, the S410S has interesting anomalies (shown in figure 4.17). For

a general description of the map, the S410S is generally deeper on average than the

expected 410 km depth and has significant shallowing in the central region of the map. More

specifically, the depth to the 410 km discontinuity beneath the Aleutian Subduction Zone

(latitude 50°-55° N) is significantly shallower than average, particularly between longitude

190°-200°E and 160°-180°E. And, just like in the P410P, this shallowing trend beneath the

subduction zone is interrupted by a deepening of the 410 between longitude 180°E and

190°E. The deepening anomaly is relatively local to 50°-55°N and 180°-190°E and trends

NW. Additional anomalies is a deepening of the 410 immediately south of continental Alaska

and the region beneath 30°-50°N.

Features in the S-wave velocity model for the 410 km discontinuity (figure 4.18) generally

correlate well with our expected thermal anomalies inferred from depth variations observed in

the S410S and P410P models. Firstly, the velocity model exhibits low S wave velocity in the


62

north and south, and anomalously high S wave velocity in the central regions beneath the

subduction zone, which matches the observed depth variations in our depth models. More

specifically, there is a strong high-velocity anomaly between 50°-55°N, between 160°-210°E,

which corresponds with the shallowing of the 410 observed in the same location.

However, there are some disparities between our depth model and the velocity model,

specifically, the region in the southwest corner (30°-50°N and 160°-190°E) is clearly a high-

velocity region that would normally be associated with a deeper 410 km, but we observe

shallowing in the 410 in our depth model. Additionally, the deepening of the 410 between 50°-

55°N and 180°-190°E contradicts the high velocity observed in the same location of the

velocity model.

Figure 4.17 Topographic map of S410S. 410 km discontinuity generally shallows in the central portions and deepens in the NE and SW portions of the map.


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Figure 4.18 S wave velocity model for 410. 410 km discontinuity exhibits high seismic velocity anomalies in the central region between latitude 50-60˚N signifying colder than average temperature mantle material.

4.5.2 660 Discontinuity

The 660 km discontinuity was only resolved in the SS seismic stacks (figure 4.19 and 4.20).

In the S660S depth model, we observed a general trend of shallowing in the 660 in the

northern and southern regions of the map and a deepening in the central areas, specifically

beneath the Aleutian Subduction Zone. Two zones of deepening anomalies are vividly

observed between latitude 50°-55°N that suggests a downward convecting mantle

(subducting slab). One on the west, between longitude 160°-180°E and the other between

longitude 190°-210°E. In between these two deepening anomalies, from longitude 180°-

190°E, a shallowing anomaly is observed, which implies upward convecting mantle. This

shallowing anomaly has a trend NE direction. Other shallowing anomalies occur directly

beneath continental Alaska, Russia, and the region between latitude 30°-50°N longitude 160°-

210°E.

The S660S depth model (figure 4.19) compares favorably with the S wave velocity model

(figure 4.20) at 660 km. In the velocity model, there are high-velocity zones in the northern


64

and southern portions of the map, while the central region beneath the subduction zone is of

anomalously low velocity. As explained earlier, lower than average velocity implies a warm

mantle, which in turn implies an upward convecting mantle, which ideally shallows the 660 km

discontinuity. A clear discrepancy between the velocity model and our depth model is the low-

velocity zone beneath continental Russia, which is not reflected in our depth map as a

deepening

Figure 4.19 Topographic map S660S. Deep anomalies are observed between latitude 50-55˚N


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Figure 4.20 S wave velocity model for 660. High-velocity anomalies can be seen between latitude 50-60˚N and in the NW corner of the map. High-velocity anomalies suggest colder than average mantle material.

Figure 4.21 P wave velocity model for 660. High-velocity anomalies can be seen between latitude 50-60˚N and in the NW corner of the map. High-velocity anomalies suggest colder than average mantle material.


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4.5.3 Transition Zone Isopach

With the S410S and S660S depth maps, an isopach of the transition zone between the

boundaries of the transition zone was produced. The average or expected thickness of the

transition zone is ~ 242 km [Flanagan and Shearer, 1998]. Our thickness model generally

shows an average transition zone thickness in the north and south parts of our study area and

an anomalously thicker transition zone in the central portions of the map. The central portions

beneath the subduction zone –latitude 50-60°N is generally 10-30 km thicker than the

average. This is expected since our S410S, and S660S depth models were respectively

shallower and deeper than average in that region. However, this trend of thicker TZ beneath

the subduction zone is not entirely continuous and breaks off between longitude 180°-190°E,

where the TZ zone is slightly below average thickness.

Figure 4.22 Thickness model for Transition Zone shows thicker than average TZ thickness between latitude 50-55˚N


67

CHAPTER V

DISCUSSION

5.1 Discussion

From the seismic profiles shown in the results section, shallow discontinuities were observed

around 100 km, 110 km, and 220 km deep (figure 5.1 and 5.2). The P100P phase has a

positive amplitude that occurs when the velocity below the interface is higher than above. The

P110P and P200P phases both have negative amplitudes. Negative phases are the result of

a low-velocity layer beneath a high velocity layer. The LAB is traditionally at 100 km in

oceanic regions but 200 km in continental. Because most of this region is oceanic, it would be

fair to assume the negative P110P phase is the LAB. We believe the region between the 100

and 200 km depths is a lithosphere-asthenosphere transition zone (LATZ) [Ainsworth et al.,

2014]. The mean depth of the LAB in the PP model is 136 km. The LAB shows up in the SS

as a low-velocity zone between ~40-110 km and not as a sharp discontinuity. The mean

depth for the LAB observed in the SS data is 92 km, which compares more favorably with

findings from [Plomerová et al., 2002]. Plomerová et al., 2002 proposed a global model of the

LAB which, suggests a LAB depth of between 70-100 km beneath the Aleutian-Bering sea

region. The depth to the LAB in the PP data was 110 km. The difference in the depths

between the PP and SS data are attributable to 1) the depth in the PP data is relative to the

ocean surface whereas the depth in the SS is relative to the ocean floor, which accounts for

about 5 km of the difference, 2) errors in the reference velocity model used and 3) the

difference in wavelengths between PP and SS data.


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Figure 5.1 PP section across longitude 176˚E showing LAB and P220P. The 410 km discontinuity appears at ~410 km and shows a shallowing beneath 50˚N.

Figure 5.2 SS section across longitude 172.5˚E showing LAB. The 660 km discontinuity appears at ~660 km

Generally, higher than average seismic velocities in the mantle corresponds to a hotter than

average mantle temperature, while lower than average seismic velocities correspond to a


69

colder than average mantle temperature. Variations in velocity can also be related to changes

in the iron content or hydration [Bina and Helffrich, 1994]. A hot mantle typically correlates to

the presence of a mantle plume or hot rising magma while a cold mantle generally associates

with the downwelling of a cold subducting slab [A Cao and Levander, 2010; Q Cao et al.,

2011]. As discussed in the literature review, the depths of the 410 km and 660 km are directly

affected by local variations in temperature where higher than average temperatures deepens

the 410 and shallows the 660. Lower than average temperatures shallows the 410 and

deepens the 660 [G. R. Foulger, 2004]. Since both seismic velocity and depth to the TZ

discontinuities are related to temperature, depth maps to the 410 and 660 should show similar

trends to anomalies observed in maps of the velocity variations for a given depth. We will be

using the velocity models generated from the GyPSum 3D Earth Model for such comparisons

in this work.

The character of the Transition Zone beneath our study area can be understood by

discussing the nature of the 410 and 660, since the response of the Transition zone to the

subducting Pacific Plate provides valuable insight in the dynamics of subduction beneath the

Aleutian Subduction Zone (ASZ). From both our P410P and S410S depth models (figure 5.3

and 5.4), we noticed a 5-20 km shallowing of the 410 beneath the Aleutian Subduction Zone

(ASZ). This shallowing of the 410 suggests the presence of the cold subducting Pacific slab.

In addition, P410P and S410S depth models correlate closely to the 410 P and S velocity

model from the GyPSuM Earth Model[Simmons et al., 2010]. The S660S also shows 10-30

km deepening beneath the Aleutian Subduction Zone (ASZ). This combined shallowing and

deepening of the 410 and 660, respectively, leads to a thickened TZ in the region of

subduction. Our isopach map of the TZ (figure 5.5) demonstrated this expected thickening.

The next region of interest –Region A- is shown in the 410 depth and velocity model (figure

5.3). Region A is a depression in the 410 and a relatively high-velocity anomaly between 50°-

55°N and 180°-170°W, which generally trends NW. Considering that the 410 phase change

has a positive Claperyon slope, deepening at the 410 strongly suggests the presence of

upward convection of hot mantle (low seismic velocity), which contradicts with the high


70

velocity observed in the velocity model. The apparent contradiction between the depth and

velocity model means anomaly A cannot be uniquely explained by thermal variation,

Claperyon slope, and phase change. Since this anomaly is positioned beneath the Bowers

ridge and only appears in the 410 models, we hypothesize that anomaly A represents

remnants of an ancient slab –possibly from the Kula plate- that sourced the Bowers island

arc. [Verzhbitsky et al., 2007] investigated the tectonics of the northern Pacific Ocean,

concluding that Bowers Ridge formed ~30 Mya, suggests that the Bowers Ridge is a remnant

of an ancient island arc, while others consider it as a Mesozoic island arc [Pitman and

Talwani, 1972], or even as a remnant of an island arc driven to its current location by the Kula

plate[Ben-Avraham and Cooper, 1981].

In regions B, the 410 beneath Alaska is depressed, and correlates with the high-velocity

anomaly observed in the velocity models (figure 5.3). Additionally, the 660 depth model

indicates a deepening underneath Alaska, which is correlated by the high-velocity anomaly

observed in the velocity model (figure 5.4). [Ai et al., 2005] mapped variations in the thickness

of the transition zone beneath south-central Alaska and found general thinning of the

transition zone, by as much as 30 km. Their results compare favorably with the TZ thinning

observed beneath Alaska in our depth models. In general, thinner TZ beneath Alaska imply

the presence of warmer mantle (low seismic velocity) due to exothermic and endothermic

nature of the 410 and 660, respectively.

In regions C and E (figure 5.4), we observe a deepening 660 km discontinuity in our depth

models and a low-velocity anomaly in our velocity model. This anomaly is expected in the

presence of down welling of cool rock (subduction) and infers that the depth anomaly is

related to the subducting Pacific slab. Considering Claperyon slope, the 660 beneath these

anomalies deepens as expected. However, the location of the anomaly in region C is

extended south of the ASZ, which suggests an irregular path of subduction. It is known that in

this part of the subduction zone, there is more strike-slip motion on the slab than reverse fault

and is characterized by an increasing subduction angle and slab rollback [Zhu and Xu, 2019].

We propose that since the Pacific Plate subducts at a high angle, it may rollback or rollover


71

on itself. Because of this unique motion, we believe the plastic subducting plate puddles on

top of the 660 beneath the ASZ and flows to lower latitudes. The S wave velocity map at the

660 km depth has a high-velocity anomaly in regions C and E, which further supports the idea

of cold slab material pooling atop the 660 below ASZ.

The depth anomalies in regions C and E show approximately a 30 km of deepening of the

660, which cannot be accounted for solely based on the thermal anomaly. Studies show that

the presence of water stored within an oceanic slab can depress the 660 by up to 15 km [A

Cao and Levander, 2010]. Moreover, using a Claperyon slope of -1.3 MPa/K [K Litasov et al.,

2005; K D Litasov, 2005], a 30 km depression in the mantle requires the Pacific plate to be

~850°K cooler than the surrounding mantle. This is unlikely since the typical average

temperature difference between a cold slab and mantle temperature at 660 km is ~670°K

(Slab temperature ~1180°K and mantle temperature ~1850°K) [A Cao and Levander, 2010].

Thus, suggesting that the ~30 km depressions observed beneath region C and E are likely

dependent on thermal anomalies and presence of water. The Transition Zone has been

shown to be a water reservoir where water –in the form of dense hydrous magnesium-silicate

phases (DHMS)- is filtered out from wet subduction slabs through a slab dehydration process

[Richard et al., 2006]. We speculate that the slab material atop the 660 is dehydrating and the

release of water contributes to the deepening of the 660 km discontinuity observed in regions

C and E.

From the isopach of the depth difference between the 660 and 410 (figure 5.5), we observe

that the transition zone is thicker than average beneath the subduction zone. This strongly

infers the presence of the subducting Pacific Plate and confirms the anti-correlated Claperyon

slopes of the 410 and 660. Nevertheless, the majority regional extent of this anomaly is

caused by deepening of the 660, since we would not expect pooling on the 410.


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Figure 5.3 (I) Topographic map of 410. (II) Velocity model of 410. ASZ (white dashed line) represents Aleutian subduction zone; A is a deepening anomaly that demarcates 410 beneath Bowers Ridge; B is a deepening anomaly that demarcates 410 beneath Alaska

Figure 5.4 (I) Topographic map of 660. (II) Velocity model of 660. ASZ (white dashed line) represents Aleutian subduction zone; C and E are deepening anomalies that demarcates pooling of high-velocity slab material, colder than average slab material at 660; D demarcates 660 beneath Alaska

I II

I II


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Figure 5.5 Thickness of transition zone. C and E show areas with thicker than average TZ where the subducting slab might be puddling and depressing the 660 significantly

5.1.1 Puddle Zones: Anomalies C and E in the Transition Zone

To further analyze anomalies C and E observed on the 660 depth and velocity models; cross-

sections were made along AA’ (170°E), BB’ (185°E), DD’ (200°E), FF’ (42°N), and GG’ (50°N)

(figure 5.7 to 5.17). Profiles AA’, DD’, and GG’ were selected because they transect

anomalous zones of significance while BB’ and FF’ cross relatively average zones and thus

serve as control profiles. Profile DD’ and AA’ show the dip view across anomalous zones E

and C, respectively, while latitude GG’ illustrates the strike view along the subduction zone


74

and the anomalous zones (C, E). Profile lines are shown in figure 5.6 below

Figure 5.6 Depth map to 660 showing anomaly C and E, and profile lines AA’ (170°E), BB’ (185°E), DD’ (200°E), FF’ (42°N), and GG’ (50°N)

In AA’, the 410 and 660 is generally flat up to 55°N after which the 410 becomes less visible,

and the 660 deepens significantly. The 520 discontinuity becomes visible after 55°N, which

suggests a change in mantle temperature. In general, the 520 is seismically less visible than

the 410 and 660 because of its low-velocity contrast and gradational Clapyeron slope, and

occurs over a wider range of 30-50 km thick [Bostock, 1996]. We suggest that the observance

of the 520 in figure 5.7 and 5.8 is a result of mantle chilling caused by the subducting slab.

We propose that the low temperature of the slab chills the mantle within the TZ, which

steepens the temperature and velocity gradient across the 520, thereby reducing the depth

range for the phase change, making the S520S phase stronger and more visible. Profile BB’

exhibits similar features as that seen in AA’. A ~30 km deepening of the 660 beneath anomaly

E, and a visible 520.

H’

H


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Figure 5.7 Profile AA’ showing 520 km discontinuity and 660 km discontinuity deepening beyond latitude 50°N (anomaly E)

Figure 5.8 Profile DD’ showing 660 deepening beneath latitude 45-50°N (anomaly E). 520 km discontinuity visible after latitude 55˚N.

Where visible in profile GG’, the 410 is elevated ~10 km, as is expected in the response of the

olivine to wadsleyite phase change[G. R. Foulger, 2004]. The 660 is generally conspicuous

the entire profile except for between 180-195° N. The deepening of the 660 to ~700 km is

observed beneath zones C and E, as shown in figure 5.9. Again, it is apparent that there are

pieces of Pacific slab material causing a deepening of the 660 beneath zones C and E.

In the control profile FF’, that does not cross C and E, the 410 and 660 are visible and

consistently flat. This gives us the confidence to infer that the respective shallowing and


76

deepening of the 410 and 660 observed in GG’ and not in FF’ is caused by the subducting

Pacific Plate.

Figure 5.9 Profile GG’ showing section across anomaly C and E. 660 deepened to ~700 km.

Figure 5.10 Control profile FF’, the 660 is observed at the expected depth of ~660 km.

Next, we look to the velocity profiles from GyPSuM to see if they exhibit the same trends

observed in our depth models. Profile AA’ shows the 660 deepens to ~690 km across

anomaly C, while the 660 in the control profile BB’, which does not cross the depth anomaly,

has a maximum depth of the 670 km. Aside from the lower than average temperature of the

subducting slab depressing the 660 [G. R. Foulger, 2004], slab dehydration within the


77

Transition Zone has also been shown to depress the 660 by up to15km [A Cao and Levander,

2010; Mohamed et al., 2014]. This observation suggests that the depth anomaly C can be

linked to cold subducting slab material atop the 660.

Figure 5.11 (A) Variation of 660 observed in profile AA’. (B)Enlargement of the dashed box around the 660 in profile AA’. The 660 is deepened by up to ~30km.

Figure 5.12 (A) Variation of 660 observed in profile BB’. (B) Enlargement of the dashed box around the 660 in profile BB’. 660 here is relatively flat with a maximum deepening of 10 km.

In the velocity profile at DD’ that crosses anomaly E, the 410 appears relatively flat, and the

660 is noticeably depressed to ~690 (figure 5.13c). A velocity reversal (feature II from figure


78

5.13b), which indicates an abrupt change in velocity trend, is partly discernable between 57°

and 63° N. We suggest that the sudden increase and subsequent decrease of velocity across

the reversal is indicative of partial melting of the Pacific slab atop the 660 km. A high-velocity

anomaly appears between 62.5-66°N seen at 520 km (feature I from figure 5.13b). The

anomalously high velocity supports the “mantle chilling effect” explained earlier where the

subducting slab cools the nearby mantle, causing a relatively sharper velocity contrast at the

520.

Figure 5.13 (A) Absolute velocity profile for longitude DD’. (B) Enlargement of the dashed box showing 410, 520, and 660. Deepening of the 660 is observed along with a velocity reversal (II) of the 660. The 520 (I) visible after latitude 60°N. (C) Enlargement of the dashed box around the 660 to show ~30 deepening on the 660 km discontinuity

GG’ (figure 5.14) shows us a strike view of velocities beneath the subduction zone that

intersects C and E. As expected, high-velocity anomalies are observed between 165°-175° N

and 195°-200° N, which coincides with depth anomalies at C and E. Moreover, the 660 is

depressed by ~20 km and ~30 km at C and E respectively.


79

Figure 5.14 (A) Absolute velocity profile for latitude GG’. (B) Enlargement of the dashed box showing high velocity on top of the 660 beneath anomaly C and E.

Studies of velocity perturbations are usually superior to absolute velocities because they may

more closely reflect temperature and (or) compositional anomalies [A Cao and Levander,

2010]. In the relative velocity profile AA’ (figure 5.15), we notice a low-velocity anomaly

between 0-200 km, which suggests hot temperature, between 55˚-66˚N. This high-temperature

anomaly is north of the subduction zone and is relatively shallow. We suggest that this

anomaly can be attributed to partial melting occurring in the shallow lithosphere as the Pacific

Plate subducts [Marsh and Carmichael, 1974]. The region below the 520 is anomalously cold,

possibly because of cooling from the subducting slab sitting at the base of the TZ. Beneath

this, we notice two sub-vertical high-velocity anomalies (with colder than average temperature)

between latitude 50°N-60°N at depths between 300-660 km, which appear to puddle after 660.

In figure 5.15 and 5.16, we show the velocity perturbation profile along AA’ and DD’,

respectively. Profile DD’ exhibits similar features as we discussed for profile AA’; firstly, there is

a shallow low-velocity anomaly beyond latitude 50°N that we attribute to partial melts sourced

from the subducting slab. The 520 is distinctly visible and it is probably due to mantle cooling

from the subducting slab. In profile DD’, we notice two sub-vertical high-velocity anomalies

(cold slab) between 50°-60°N that terminate at ~660 km. Finally, in the velocity perturbation

profile GG’, we observe that the TZ beneath the Aleutian Subduction Zone is of higher than

average velocity inferring colder than average temperatures. These observations imply the

presence of cold subducting material in the TZ beneath the subduction zone.


80

Figure 5.15 S-Wave velocity perturbation profile AA’. High-velocity anomalies are observed between latitude 50-55°N (anomaly C). The deepening of the 660 is noticed between latitude 50-65°N.

Figure 5.16 S-Wave velocity perturbation profile DD’. High-velocity anomalies are observed between latitude 50-60°N (anomaly C). The deepening of the 660 is noticed between latitude 50-60°N. The 520 km discontinuity is visible beyond latitude 60°N.


81

Figure 5.17 S-Wave velocity perturbation profile GG’. The TZ in profile GG’ (across anomaly C and E) is generally of high velocity suggesting a cold TZ


82

CHAPTER VI

CONCLUSIONS

6.1 Geological Implications

An analysis of the distribution of seismic attributes around the Alaska Subduction region was

conducted, and from our results and discussion, we suggest the following conclusions:

1. The 410 (olivine to wadsleyite phase change) shallows by up to 20 km, the 660

(ringwoodite to perovskite and ferropericlase phase change) deepens by up to 30 km.

This results in a thicker than average Transition Zone and suggests a cooler than

average Transition Zone beneath the Aleutian subduction zone.

2. Our geophysical models of the study area show that the 410 deepens underneath

Bowers Ridge, which suggests upward convection of hot mantle (low seismic

velocity). However, the velocity model is at odds with the depth model, instead high

velocities infer a cold mantle (high seismic velocity) beneath the Bowers ridge. We

hypothesize that this anomaly represents remnants of an ancient slab –possibly from

the Kula plate- that sourced the Bowers island arc.

3. Based on the depth model, velocity models, and the thicker than usual TZ from SdS

depth estimates, we suggest that the subducting Pacific slab pools atop of the 660,

causing it to deepen due to the presence of the plastic cold slab. However, the depth

anomaly on the 660 cannot be explained by temperature anomaly alone, and we

believe it is further depressed because of dehydration of the slab.

4. Beyond latitude 60°N, the 520 was regularly observed in our seismic profiles. We

suggest that the visibility of the 520 in close vicinity to the subducting slab is because

of the “mantle chilling effect” at the base of the TZ. We believe the “mantle chilling

effect” caused by the hypothesized slab puddling on the 660 increases the velocity

contrast in that region resulting in a sharp 520 discontinuity.


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Figure 6.1 Cartoon model of Pacific plate subduction beneath the Aleutian subduction zone across profile HH’. Shows elevated 410, 520, and deepened 660. Convective current and mantle chilling leads to sharp 520. Slab dehydration contributes to ~30km deepening observed on the 660, and possible slab rollback on the 660 accounts for cold anomaly seen beneath the ASZ at lower latitude.


84

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APPENDICES

Appendix A – Processing Workflow and Codes

SAC to MAT Conversion

IRIS DMC saves time series PdP and SdS data in a sac format. SACtoMATB_2015 converts

data from sac to mat, which is compatible in Matlab. Below is SACtoMATB_2015 and its

subroutine SACTOOLB_2015.

function SACtoMATB_2015(outP) %This program will convert your SAC files into 3 component mat files

and %put them in a new directory called dirsort.

%ex: dirsortcl_2011 nowdir=pwd; islash=find(nowdir=='\'); outdir3=[outP ':\MAT' nowdir(islash(2):end)] outdir1=[outP ':\MAT1' nowdir(islash(2):end)] mkdir(outdir3) mkdir(outdir1)

A=dir('*BHZ*.SAC'); %searches current directory for all BHZ files,

sets up structural array which holds info for each file O=length(A); %number of BHZ files found z=0; oldname='ZZZZZZZZZZZZZ'; for i=1:O; %runs loop for all BHZ files

fnameZ=A(i).name %searches the structural array 'A' for the file

name if fnameZ(1:13)==oldname disp('already did a station by that name') else whereBHZ=findstr(fnameZ,'BHZ'); fname=dir([fnameZ(1:(whereBHZ-1)) '*'

fnameZ((whereBHZ+3):end-10) '*']);

if length(fname) ==3 SACTOOLB_2015(fname,outdir3); %runs SACTOOL2010C program end if length(fname) ~=3 fname(1).name=fnameZ; SACTOOLB_2015(fname,outdir1); %runs SACTOOL2010C program end end oldname=fnameZ(1:13); end

function SACTOOL_2015(filename,out_path) %MatTimes Tool:


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% %SACTOOL Imports Seismic Analysis Code files (SAC). % 1) SACTOOL(FILENAME,DIRECTORY) Imports a single file from the SAC % format to a Matlab data file (.mat) as a structured array

(variable % with fields). The output name is the same as the string argument % FILENAME and the file is saved in the current directory.

DIRECTORY % is a string argument naming the directory (i.e.,

'C:/MatTimes/Data/'). % If DIRECTORY is left off, it is assumed that the current

directory % contains the file. % % 2) SACTOOL(LIST,DIRECTORY) Imports multiple files contained within

the list. % To generate LIST, simply use the Matlab DIR command like the % following: % % LIST=dir(FILES) % % where FILES is a string argument that uses the wildcard (*) to % make a list. LIST will be a structured array consisting of one % column. % % 3) SACTOOL(LIST3,DIRECTORY,NAMES) % LIST3 is similar to LIST except that it is designed to load in

three % seismic components to save to a single file as a multi-

dimensional % structured array. NAMES is a structured list of file names for

each % row in LIST3. A row of files in LIST3 will be combined into a

single % file in the corresponding row or column in NAMES. The three

components % will saved within NAMES as data(1:3) where "data" is a structured

array. % See example below. % % IMPORTANT NOTES: % % To choose a particular SAC format (user-defined header fields),

four % inputs are necessary: SACTOOL(FILE,DIRECTORY,OUTNAME,FLAG). % % For TTU format: FLAG = 1 % User5, User6, User7 and User8 must have actual event time

information % so that MatTimes can determine travel time markers for the

imported % data stream. User5 = Julian Day, User6 = event hour, User7 =

event minute, % User8 = event seconds. All of these fields will be combined

during % importation to a field named "evtime". The actual data stream

beginning % time will be in the field named "begintime". "Streamtime" will

contain % the data stream length in seconds. %


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% For RISTRA format: FLAG = 2 % Most likely format to use. % % % EXAMPLES for PC: % % %Load in a z-component data file % load dir_path %(file generated with SETUP with path names for

example only) % FILENAME=[whereami '/Data/event0920z']; %(whereami from

dir_path) % SACTOOL2007(FILENAME) %create Matlab file % % %Load in all three components for the same event, 0920. % load dir_path %(this does not have to be used in real

situations) % DIRECTORY=[whereami '/Data/']; % LIST=dir([DIRECTORY 'event0920*']); % SACTOOL2007(LIST,DIRECTORY) % % %Load in three components for a multi component file creation % %Try this in the "Data" directory (delete all example ".mat"

files first) % LIST3=[dir('ev*e') dir('ev*n') dir('ev*z')]; % DIRECTORY=[]; % NAMES.name='event0920'; % SACTOOL2007(LIST3,DIRECTORY,NAMES); % %look at some input % load(NAMES(1).name) % data(2) % plot3comp(NAMES(1).name) % % %if LIST3 is long, it is not convenient to type in all output

names, in such % %a case you could do a form of the following for your file

name structures: % % LIST3=[dir('ev*e') dir('ev*n') dir('ev*z')]; %make data file

list % [a,b]=size(LIST3); % for i=1:a % lname=length(LIST3(i).name); % NAMES(i).name=LIST3(i).name(1:lname-1) %use same name

minus last character % %as a

file name % end % % %disp(['SACTOOL...Working']); %directory is ./ if nargin=1

jout=0 [b]=length(filename)

%loop through components for j=1:b; %determine string file name name=[filename(j).name] fname=[name]; [abort,h1,h2,h3,data2]=READSAC_2015(name); %read file\


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if abort==666, break, end tsec=h2(6)./(1000); sec=h2(5)+tsec; nztime=[h2(1:5)]; nztime(5)=sec; nzdeltatime=h1(8); nzdeltamin=ceil(nzdeltatime/60); nzdeltasec=nzdeltatime-60*nzdeltamin; evtime=nztime+[0 0 0 nzdeltamin nzdeltasec]; if evtime(5) < 0, evtime(4)=evtime(4)-1; evtime(5)=evtime(5)+60;

end if evtime(4) < 0, evtime(3)=evtime(3)-1; evtime(4)=evtime(4)+60;



end % evtime=[h(1:5)] %begintime=[h(1) h(24:26) round(h(27)*100000)/100000];

filetime=jconvnamehg(name); begintime=nztime; streamtime=round(h1(7)*100000)/100000;

fields=['name','import','evtime','begintime','nztime','filetime','str

eamtime','station_lat','station_lon',...

'station_elevation','borehole_depth','compass_az','compass_inc','even

t_lat','event_lon','event_depth','magnitude',...

'arc_distance','azimuth','backazimuth','gcarc','deltatime','SACT0','S

ACT1',... 'SACT2','SACT3','SACT4','SACT5','SACT6','dt','data']; % h=[h2(1:6) round(h1(1)*1000000)/1000000 h1(6=h8:7)

h1(11=h10:17) h1(32=h17:37=h22) h1(39) h1(46=h24:54) h1(58=h33:59)];

fill=[fname,datestr(datenum(now)),evtime,begintime,nztime,filetime,st

reamtime,h1(32),h1(33),...

h1(34)/1000,h1(35),h1(58),h1(59),h1(36),h1(37),h1(39),h1(50),... h1(51),h1(53),h1(52),h1(54),nzdeltatime,h1(12),h1(13),...

h1(14),h1(15),h1(16),h1(17),h1(18),(floor(h1(1)*1000000)/1000000),[da

ta2(:)']];

d=cell2struct(fill,fields,2); data(j)=d; %if j == 1, hold off, plot(data2,'r'), end %if j == 2, hold on, plot(data2,'b'), end %if j == 3, hold on, plot(data2,'g'), end

end if abort==666, evtime(1)=-10, end if evtime(1) > 0; failflag=0; Devent=[data(1).event_lat data(1).event_lon]; Dsta=[data(1).station_lat data(1).station_lon]; fnameoutb=[out_path '\' [fname(1:end-3) '.mat']]; save(fnameoutb,'data');


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fclose('all'); %avoid "too many files open" problem else failflag=1; fname=[fname ' failed to read']; end

%pause

%close(wb) %disp(['SACTOOL...Done'])


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Cutting

ALLCUT_2015 cuts both PdP and SdS data. It calls on the sub-routine CUTDATA_2015 to

perform cuts. Below is the code for ALLCUT_2015 and CUTDATA_2015.

function ALLCUT_2015(phase,cutdata_window,istart,ftype)

% ALLCUT_2015(phase,cutdata_window,istart,ftype,network) % ALLCUT_2015('P',[30 150],1,'BH','*') % ALLCUT_2015('SdS',[350 120],1,'BH','*') % % warning off all %will disable warnings that tell us the directory we

are trying to create already exists

dirs=dir('*') home=pwd for i=istart:(length(dirs)-1) i=i; if exist(dirs(i).name) == 7 dir1=dirs(i).name; if dir1(1)=='.' disp(['did not process ' dir1]) else cd(dir1); %save(MASTER_PATH,'events') % disp(['Events left is ' events_left '.']) events=dir(['*' ftype '*.mat']); outd=pwd; imat=findstr(outd,'MAT'); outd=['G:\CUT_' phase outd(imat+3:end)]

CUTDATA_2015(events,phase,[],'iasp91',cutdata_window,outd); %runs

program cd(home) i=i; dir1=dirs(i).name; save(['start_all_' phase],'i') end end end

end

function CUTDATA_2015(file,phase,variable,model,window,outd) %MatTimes Tool: % %CUTDATA Cuts data about an input phase based on a time window. % 1) CUTDATA(LIST,PHASE,VARIABLE,MODEL,WINDOW,MODE) % Cuts down data within the LIST about PHASE according to % WINDOW. LIST is a structured list naming each file to % be snipped. See SACTOOL option 2 for LIST creation. PHASE % is a string argument naming the phase to center about (i.e., % 'P'). If the phase has a depth variable, such as Pds, the % input VARIABLE must be given as a value. Otherwise, VARIABLE % should be an empty cell ([]). MODEL is the velocity model % to use when determining the phase marker. WINDOW is the


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% window about the phase marker to keep. WINDOW(1) is the % pre-phase time window to keep in seconds. WINDOW(2) is the % post-phase time to keep in seconds. MODE determines whether % or not the Matlab data file is deleted. % % % NOTE: The header information will be adjusted to the cut. Also, % if the data file has three components, all will be cut and % all headers will be adjusted accordingly. If the given phase % has multiple times, the cut will be about the minimum and % maximum times expanding the total stream time. % % ex: CUTDATA_2014(dir('*.mat'),'P',[],'iasp91',[30

150],'c:\P_MAT\neme..')

goback=pwd; %stores current directory as a variable shorttest=0; dirflag=0; for j=1:length(file); %will loop for all files

name=file(j).name; %searches structural array for first file and

sets name as the file name namem=name; %sets namem as name load([name ]); gcarc=data(1).gcarc [t,rp]=timetool(phase,variable,model,[data(1).event_lat

data(1).event_lon data(1).event_depth],[data(1).station_lat

data(1).station_lon data(1).station_elevation]); %runs TIMETOOL_2010C

program runtest=isnan(t); %checks to make sure 't' is a number if runtest == 0; %if runtest returns a number load([name ]); %loads the file parameters (name, event time,

azimuth, etc.) into Matlab which allows us to pull info from it gcarc99=data(1).gcarc; %pulls the great circle arc distance

from structural array distance from event to station howmany=length(data); %number of components (i.e. 3 component

being BHE BHN and BHZ info stored in structural array) save_flag=0; for i=1:howmany %for all components eventtime=sum(data(i).evtime.*[366*24*60*60 24*60*60

60*60 60 1]); %converts event time to seconds Ptime=eventtime+t(1); %time of first P arrival oldbegintime=sum(data(i).begintime.*[366*24*60*60

24*60*60 60*60 60 1]); %beginning of data collection deltatime=Ptime-oldbegintime; %time difference between

Ptime and oldbegintime data(i).streamtime=(length(data(i).data)-1)*data(i).dt; if deltatime < window(1) | data(i).streamtime <

deltatime+sum(window); %if your amount of data is smaller than your

window, will not cut save_flag=1; %disp(['Window too big to cut; data stream not long

enough. File is ' name '.']) else olddata=data(i).data; %actual seismogram data dt=data(i).dt; %sample rate (ex: 40 samples a second

= 1/40 which is .025) newbegintime=Ptime-window(1); %cuts data with

specified window time cutlowendtime=newbegintime-oldbegintime; %data from

old begin time to new begin time


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cutlowsample=ceil(cutlowendtime/dt); %number of

samples cutendsample=floor(cutlowsample+(sum(window)/dt));

%number of samples streamtime=data(i).streamtime; %length (time) of data newdata=olddata(cutlowsample:cutendsample); %number

of samples in entire data set data(i).data=detrend(newdata); %detrends data data(i).streamtime=(length(newdata)*dt)-dt; [year,jd,hr,mn,sec]=SEC2YDHMS(newbegintime);

%converts back to original time (not seconds) data(i).begintime=[year jd hr mn sec]; data(i).deltatime=newbegintime-eventtime; %just to

keep track of how much was cut data(i).SACT0=window(1); %SACT0 is Pwave arrival time

relative to first sample (seconds) data(i).SACT1=window(2); %SACT1 is length of data

stream from direct P to end of stream in seconds end end if save_flag==0; %if all components were cut dirflag=dirflag+1; if dirflag==1; mkdir(outd), end outname=[outd '\' phase '_' name ]; save(outname,'data'); %saves data set end end end

%mkdir cutdata %makes new directory called cutdata %mover=[phase '*']; %movefile(mover,'cutdata'); %moves created data to new directory

SdS Rotation

ALL_SdS_SVD_ROT_2015 calls on SdS_SVDROT_2015 to perform a singular value

decomposition rotation on SdS data.

function ALL_SdS_SVD_ROT_2015(outP) % ALL_SdS_SVD_ROT_2015('G') % % % warning off all %will disable warnings that tell us the directory we

are trying to create already exists

dirs=dir('*') home=pwd for i=1:length(dirs) i=i; dir1=dirs(i).name if dir1(1)=='.' disp(['did not process ' dir1]) elseif exist(dirs(i).name)==7 cd(dir1); pwd %save(MASTER_PATH,'events') % disp(['Events left is ' events_left '.'])


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out_path=pwd; out_path=[outP ':\SdS_SVDR' out_path(3:end)] SdS_SVDROT_2015(out_path) %runs program cd(home) end end

end

function data=SdS_SVDROT_2015(data) % M= the list of files to process

% start rot stwin=round((data(1).SACT0-10)/data(1).dt); endwin=round((data(1).SACT0+50)/data(1).dt); D=data; D(1)=data(2); D(2)=data(3); D=GFILT_struc_2015(D,.025,[5 5],'h'); D=GFILT_struc_2015(D,.5,[5 5],'l'); % plot(D(1).data,'b') % hold on % plot(D(2).data,'m') cd=cov(D(1).data(stwin:endwin),D(2).data(stwin:endwin)); [v,d]=eig(cd);

[NM]=[data(2).data' data(3).data']*inv(v);

data(4)=data(3); data(4).data=NM(:,2)'; % % plot(data(4).data,'c','linewidth',2) % plot(-NM(:,1),'g','linewidth',2) % title(['GCARC=' num2str(data(1).gcarc)])

Signal to Noise Ratio

Call_findSN_PdP and subroutine findSN_PdP to find signal to noise ratio for PdP data.

Call_findSN_SdS and subroutine findSN_SdS to find signal to noise ratio for SdS data.

function [SNINFO] = Call_findSN_PdP(disk)


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%Call_findSN calls findSN_PdP which calculates the signal to noise

ratio

files1=dir; home1=pwd; for m=3:length(files1) cd(files1(m).name) files2=dir; home2=pwd; mkdir([disk home2(2:3) 'S' home2(5:end)]) mkdir(['H' home2(2:3) 'S' home2(5:end)]) for n=3:length(files2) cd(files2(n).name) home3=pwd mkdir([disk home3(2:3) 'S' home3(5:end)]) mkdir(['H' home3(2:3) 'S' home3(5:end)])

[StoN_peak_high,StoN_std_high,StoN_peak_low,StoN_std_low,fileout,EVin

fo,STinfo,GCarc]=findSN_PdP(disk); if m==3 && n==3 SNINFO.StoN_peak_high=StoN_peak_high; SNINFO.StoN_std_high=StoN_std_high; SNINFO.StoN_peak_low=StoN_peak_low; SNINFO.StoN_std_low=StoN_std_low; SNINFO.fileout=fileout; SNINFO.STinfo=STinfo; SNINFO.EVinfo=EVinfo; SNINFO.GCarc=GCarc; else SNINFO.StoN_peak_high=[SNINFO.StoN_peak_high

StoN_peak_high]; SNINFO.StoN_std_high=[SNINFO.StoN_std_high

StoN_std_high]; SNINFO.StoN_peak_low=[SNINFO.StoN_peak_low

StoN_peak_low]; SNINFO.StoN_std_low=[SNINFO.StoN_std_low StoN_std_low]; SNINFO.fileout=[SNINFO.fileout fileout]; SNINFO.STinfo=[SNINFO.STinfo; STinfo]; SNINFO.EVinfo=[SNINFO.EVinfo; EVinfo]; SNINFO.GCarc=[SNINFO.GCarc GCarc]; end cd(home2) end cd(home1) dout=['H' home1(2:3) 'S' home1(5:end) '\SNOUT_PdP'] save(dout,'SNINFO') dout=['G' home1(2:3) 'S' home1(5:end) '\SNOUT_PdP'] save(dout,'SNINFO') end dout=['H' home1(2:3) 'S' home1(5:end) '\SNOUT_PdP'] save(dout,'SNINFO') dout=['G' home1(2:3) 'S' home1(5:end) '\SNOUT_PdP'] save(dout,'SNINFO') end


98

function


fo,STinfo,GCarc]= findSN_PdP(disk) %This code was written on 5/14/2019. This code takes a PdP matlab

file as input %and returns the peak and standard deviation signal to noise ratio of

the original trace as %SToN_peak_high and SToN_std_high. It also returns the peak and

standard deviation signal to %noise ratio of the low pass filtered trace as SToN_peak_low and %StoN_std_low. %Because the PdP data is in the vertical channel, the North South and

East West channels are discarded before processing files=dir home=pwd for n0=3:length(files) n=n0-2; file=files(n0).name; fileout(n).name=[home '/' file]; fileout(n).name(1)=disk; fileout(n).name(4)='S' load(file) EVinfo(n,:)=[data(1).event_lat data(1).event_lon

data(1).event_depth]; STinfo(n,:)=[data(1).station_lat data(1).station_lon

data(1).station_elevation]; GCarc(n)=data(1).gcarc; %files is the current open directory %the for loop runs through all the trace files in the directory

and stores %the file in n0 and calculates the signal to noise ratio

% size(data) data=data(3); %data is equal to data(3) because that is the vertical channel in

which %the PdP wave travels % size(data) time=[0:1:(length(data.data)-1)]*data.dt; %what is data?

isig0_1=(data.SACT0-5)/data.dt; isig0_2=isig0_1+70/data.dt; %isig0_1 is the starting point of where we selected as our

signal. A %margin of 5s is used should the signal arrive earlier %%isig0_2 is the end of our signal

inoise0_1=(data.SACT0-105)/data.dt; inoise0_2=inoise0_1+70/data.dt; %Same as the signal, inoise0_1 and inoise0_2 are the bounds of

the %signal considered as the signal

signal_peak=max(abs(data.data(isig0_1:isig0_2))); noise_peak=max(abs(data.data(inoise0_1:inoise0_2))); StoN_peak_high(n)=signal_peak/noise_peak; %the code above calculates the signal to noise ratio using the

peak


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%signal value in the signal range and the peak noise value in the

noise range

signal_std=std((data.data(isig0_1:isig0_2))); noise_std=std((data.data(inoise0_1:inoise0_2))); StoN_std_high(n)=signal_std/noise_std; %The above code caomputes the signal to noise ratio of the trace

by %using standard deviation of the signal and the standard

deviation of %the noise %

dlow=GFILT_data_2015(data.data,0.05,[5 5],data.dt,'l'); %runs

GFILT program low pass filter

signal_peak=max(abs(dlow(isig0_1:isig0_2))); noise_peak=max(abs(dlow(inoise0_1:inoise0_2))); StoN_peak_low(n)=signal_peak/noise_peak; %The above block of code first low pass filters the trace and

removes %high frequency data before calculating the signal to noise

ratio. %StoN_peak_low is the signal to noise ratio using the max signal

and %max noise after low pass filtering the trace. % % signal_std=std((dlow(isig0_1:isig0_2))); noise_std=std((dlow(inoise0_1:inoise0_2))); StoN_std_low(n)=signal_std/noise_std; %the above code calculates the signal to noise ratio on the low

pass %filtered trace by using the standard deviation method. save(fileout(n).name,'data') save(['H' fileout(n).name(2:end)],'data') end

end

function [SNINFO] = Call_findSN_SdS(disk) %Call_findSN calls findSN_SdS which calculates the signal to noise

ratio

files1=dir; home1=pwd; for n=3:length(files1) cd(files1(n).name) files2=dir; home2=pwd; mkdir([disk home2(2:3) 'S' home2(5:end)]) mkdir(['H' home2(2:3) 'S' home2(5:end)]) mkdir(['F' home2(2:3) 'S' home2(5:end)]) for m=3:length(files2)


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cd(files2(m).name) home3=pwd mkdir([disk home3(2:3) 'S' home3(5:end)]) mkdir(['H' home3(2:3) 'S' home3(5:end)]) mkdir(['F' home3(2:3) 'S' home3(5:end)])


fo,STinfo,GCarc]=findSN_SdS(disk); if m==3 && n==3 SNINFO.StoN_peak_high=StoN_peak_high; SNINFO.StoN_std_high=StoN_std_high; SNINFO.StoN_peak_low=StoN_peak_low; SNINFO.StoN_std_low=StoN_std_low; SNINFO.fileout=fileout; SNINFO.STinfo=STinfo; SNINFO.EVinfo=EVinfo; SNINFO.GCarc=GCarc; else SNINFO.StoN_peak_high=[SNINFO.StoN_peak_high

StoN_peak_high]; SNINFO.StoN_std_high=[SNINFO.StoN_std_high

StoN_std_high]; SNINFO.StoN_peak_low=[SNINFO.StoN_peak_low

StoN_peak_low]; SNINFO.StoN_std_low=[SNINFO.StoN_std_low StoN_std_low]; SNINFO.fileout=[SNINFO.fileout fileout]; SNINFO.STinfo=[SNINFO.STinfo; STinfo]; SNINFO.EVinfo=[SNINFO.EVinfo; EVinfo]; SNINFO.GCarc=[SNINFO.GCarc GCarc]; end cd(home2) end cd(home1) dout=['H' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') dout=['G' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') dout=['F' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') end dout=['H' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') dout=['G' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') dout=['F' home1(2:3) 'S' home1(5:end) '\SNOUT_SdS'] save(dout,'SNINFO') end

function


fo,STinfo,GCarc]= findSN_SdS(disk) %This code was written on 5/14/2019. This code takes a SdS matlab

file as %input.


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%And returns the peak and standard deviation signal to noise ratio of

the original trace as %SToN_peak_high and SToN_std_high. It also returns the peak and

standard deviation signal to %noise ratio of the low pass filtered trace as SToN_peak_low and %StoN_std_low.

%Because the SdS data travels in the North South and East West

channels, the %vertical channel is discarded before processing. %SdS_SVDROT_2019 is called in this function to determine the dominant %channel of the SdS file by using Singular Value Decomposition(SVD).

files=dir; home=pwd; for n0=3:length(files) n=n0-2; file=files(n0).name; fileout(n).name=[home '/' file]; fileout(n).name(1)=disk; fileout(n).name(4)='S' load(file) EVinfo(n,:)=[data(1).event_lat data(1).event_lon

data(1).event_depth]; STinfo(n,:)=[data(1).station_lat data(1).station_lon

data(1).station_elevation]; GCarc(n)=data(1).gcarc; %files is the current open directory %the for loop runs through all the trace files in the directory

and stores %the file in n0 and calculates the signal to noise ratio % size(data) d1=data(1).data; d2=data(2).data; %d1 stores the East West channel %d2 stores the North South channel % size(data)

inoise0_1=round((data(1).SACT0-160)/data(1).dt); inoise0_2=round(inoise0_1+120/data(1).dt); isig0_1=round((data(1).SACT0-5)/data(1).dt); %why only data(1)?? isig0_2=round(isig0_1+90/data(1).dt); %why only data(1)?? %isig0_1 is the starting point of where we selected as our

signal. A %margin of 5s is used should the signal arrive earlier %%isig0_2 is the end of our signal %the entire signal is plotted in red

d1low=GFILT_data_2015(d1,0.05,[5 5],data(1).dt,'l'); %runs GFILT

program low pass filter d2low=GFILT_data_2015(d2,0.05,[5 5],data(2).dt,'l'); %runs GFILT

program low pass filter. Why data(1)?

[dsvd1,dsvd2,rotmhigh]=SdS_SVDROT_2019(d1(isig0_1:isig0_2),d2(isig0_1

:isig0_2)); %calls SdS_SVDROT_2019. This takes in the signal %from d1 and d2 as inputs and returns the singular value

decomposed %signals dsvd1 and dsvd2 as well as the rotmhigh (rotation

matrix).


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[dsvd1low,dsvd2low,rotmlow]=SdS_SVDROT_2019(d1low(isig0_1:isig0_2),d2

low(isig0_1:isig0_2)); %calls SdS_SVDROT_2019. This takes in the low

pass filtered signal %from d1low and d2low as inputs and returns the singular value

decomposed %signals dsvd1low and dsvd2low as well as the rotm (rotation

matrix).

if (max(dsvd1low)-min(dsvd1low)) > (max(dsvd2low)-min(dsvd2low)) picksvd=1; svdcolor='g'; else picksvd=2; svdcolor='c'; end %This if statement determines which direction the maximum

polarization %of signal is, whether dsvd1 or dsvd2.

[NM]=[d1' d2']*rotmlow; d1rotm=NM(:,2); d2rotm=NM(:,1); %NM stores the rotated d1 and d2 data

data=data(1); if picksvd==1 data(1).data=d1rotm; else data(1).data=d2rotm; end %This if statement selects the data with the maximum polarization

based %on the SVD

% save(fileout(n).name,'data') % save(['H' fileout(n).name(2:end)],'data')

signal_peak=max(abs(data(1).data(isig0_1:isig0_2))); noise_peak=max(abs(data(1).data(inoise0_1:inoise0_2))); StoN_peak_high(n)=signal_peak/noise_peak; %the code above calculates the signal to noise ratio using the

peak %signal value in the signal range and the peak noise value in the

noise range

signal_std=std((data(1).data(isig0_1:isig0_2))); noise_std=std((data(1).data(inoise0_1:inoise0_2))); StoN_std_high(n)=signal_std/noise_std; %The above code computes the signal to noise ratio of the trace

by %using standard deviation of the signal and the standard

deviation of %the noise

d1low=GFILT_data_2015(data(1).data,0.05,[5 5],data(1).dt,'l');

%runs GFILT program low pass filter.


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d1low=GFILT_data_2015(data(1).data,0.05,[5 5],data(1).dt,'l');

%runs GFILT program low pass filter. signal_peak=max(abs(d1low(isig0_1:isig0_2))); noise_peak=max(abs(d1low(inoise0_1:inoise0_2))); StoN_peak_low(n)=signal_peak/noise_peak;

signal_std=std((d1low(isig0_1:isig0_2))); noise_std=std((d1low(inoise0_1:inoise0_2))); StoN_std_low(n)=signal_std/noise_std;

end end

function [d1,d2,v]=SdS_SVDROT_2019(d1,d2) cd=cov(d1,d2); [v,d]=eig(cd); [NM]=[d1' d2']*v; d1=NM(:,2); d2=NM(:,1); end

Quality Check

“PdP_SdS_QC_2019_reg” runs through PdP/SdS data and sorts them into good, bad, and

borderline based on signal to noise ratio.

function PdP_SdS_QC_2019_reg(disk,phase) %UNTITLED Summary of this function goes here % Detailed explanation goes here % % for PdP events we will use % PdP_SdS_QC_2019('G','PdP',65) % if your disk is E put 'E' ...... % % for PdP events we will use % PdP_SdS_QC_2019('G','SdS',80) % if your disk is E put 'E' ...... % % load(['SNOUT_QCed_' phase '.mat']) if phase=='SdS' gcmin=70; pret=250; else gcmin=75; pret=150; end num_keep=1000; n0=length(SNINFO.keep); if n0 > 3.5, n0=n0-3; end nall=length(SNINFO.fileout)

for n=n0:nall file=SNINFO.fileout(n).name; file=[disk file(2:end)] load(file)


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dlow=GFILT_data_2015(data.data,0.05,[5 5],data(1).dt,'l'); keep_flag=0; if SNINFO.StoN_peak_high(n)>1.2, keep_flag=keep_flag+1; end if SNINFO.StoN_std_high(n)>1.5, keep_flag=keep_flag+1; end if SNINFO.StoN_peak_low(n)>3, keep_flag=keep_flag+1; end if SNINFO.StoN_std_low(n)>3, keep_flag=keep_flag+1; end if SNINFO.StoN_peak_low(n)>10, keep_flag=keep_flag+1; end if SNINFO.StoN_std_low(n)>8, keep_flag=keep_flag+1; end if SNINFO.GCarc(n)<gcmin, keep_flag=0; end

tot_SN=SNINFO.StoN_peak_high(n)+SNINFO.StoN_std_high(n)+SNINFO.StoN_p

eak_low(n)+SNINFO.StoN_std_low(n); tot_SN_low=SNINFO.StoN_peak_low(n)+SNINFO.StoN_std_low(n); delay_flag=max(abs(dlow(3400:4250)))/max(abs(dlow(4250:4699))); if delay_flag<0.85 keep_flag=0; tot_SN_low=0; tot_SN=0; % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(data.data)))*data.data,'r','linewidth',1) % hold on % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(dlow)))*dlow,'r','linewidth',3) end

if keep_flag>=4 || tot_SN_low >= 8 || tot_SN > 10.5 if tot_SN > 16 || tot_SN_low > 8 % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(data.data)))*data.data,'y','linewidth',2) % hold on % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(dlow)))*dlow,'g','linewidth',3) % title(['GCarc=' num2str(SNINFO.GCarc(n)) '

StoN peak high=' num2str(SNINFO.StoN_peak_high(n)) ' StoN std high='

num2str(SNINFO.StoN_std_high(n)) ' StoN peak low='

num2str(SNINFO.StoN_peak_low(n)) ' StoN std low='

num2str(SNINFO.StoN_std_low(n)) ' depth=' num2str(data.event_depth)

'keep_flag=' num2str(keep_flag)]) SNINFO.keep(n)=1; hi='A-listed' else plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(data.data)))*data.data,'b','linewidth',1) grid on hold on plot([0 (length(data.data)-1)]*data.dt,[0

0],'k','linewidth',3) plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(dlow)))*dlow,'k','linewidth',2) hold on plot([data.SACT0 data.SACT0],[-1 1],'c','linewidth',3) plot([data.SACT0 data.SACT0]-pret,[-1

1],'c','linewidth',3) title(['GCarc=' num2str(SNINFO.GCarc(n)) ' StoN peak

high=' num2str(SNINFO.StoN_peak_high(n)) ' StoN std high='



num2str(SNINFO.StoN_std_low(n)) ' depth=' num2str(data.event_depth)

'keep_flag=' num2str(keep_flag)]) tkeep_flag=777;


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ntest=floor(n/5)*5; while tkeep_flag~=0 tkeep=input('enter a 1 to keep (or just hit enter) or

a 0 to toss enter 666 to save and quit') if length(tkeep) < 0.5 tkeep=1; end hi='oops' if tkeep==1, tkeep_flag=0; end if tkeep==0, tkeep_flag=0; end if tkeep==666, tkeep_flag=0; end end % tkeep=tkeep SNINFO.keep(n)=tkeep; if n == floor(n/num_keep)*num_keep || tkeep == 666 hi='saving' n=n save(['SNOUT_QCed_' phase '.mat'], 'SNINFO') end hold off end else SNINFO.keep(n)=0; hi='tossed' % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(data.data)))*data.data,'r','linewidth',2) % hold on % plot([0:(length(data.data)-

1)]*data.dt,(1/max(abs(dlow)))*dlow,'k','linewidth',2) % plot([data.SACT0 data.SACT0],[-1

1],'c','linewidth',3) % plot([data.SACT0 data.SACT0]-pret,[-1

1],'c','linewidth',3) % title(['GCarc=' num2str(SNINFO.GCarc(n)) '

StoN peak high=' num2str(SNINFO.StoN_peak_high(n)) ' StoN std high='



num2str(SNINFO.StoN_std_low(n))]) % pause end hold off save(['SNOUT_QCed_' phase '.mat']) end

end

Deoceaning codes for PdP

“PdP_RM_OCEAN_2020” and sub-routine “timetool”, “runRTreflectionWD” deocean PdP

data.

function PdP_RM_OCEAN_2020(inD,process_stage,len_data,WL) %PdP_RM_OCEAN_2020('H','D',3001,0.1)


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%UNTITLED The purpose of this program is to remove ocean reflection

effects %from PdP data % iwet: number of wet events with midpoints in the ocean, % EVENT: All events in the directory % MIDPT: bouncepoints % mzWB: average water depth % Do: unfiltered/raw waveform % Do_S: deconvolved original PdP signal after removing ocean effect

and removes high frequency artifacts

load('WTOPO.mat') load('SNINFO_good_PdP.mat')

n_all=length(SNINFO.keep); for i=1:n_all [ARCLEN, AZ] =

distance(SNINFO.STinfo(i,1),SNINFO.STinfo(i,2),SNINFO.EVinfo(i,1),SNI

NFO.EVinfo(i,2));

[SNinfo.Mlat(i),SNinfo.Mlon(i)]=reckon(SNINFO.STinfo(i,1),SNINFO.STin

fo(i,2),0.5*ARCLEN, AZ); rlat=SNinfo.Mlat(i); [~,ilat]=min(abs(tlat-rlat)); rlon=SNinfo.Mlon(i); if rlon < 0, rlon=rlon+360; end [~,ilon]=min(abs(tlon-rlon)); SNINFO.MZ(i)=WTOPO(ilat,ilon)/1000;

if SNINFO.MZ(i) < 0

Fn=SNINFO.fileout(i).name; Fn(1)=inD; Fn(4)=process_stage load(Fn) dt=data(1).dt; data_360=data(8).in_rad;

if data_360 > 0

dist=distance(SNINFO.EVinfo(i,1),SNINFO.EVinfo(i,2),SNINFO.Mlat(i),SN

INFO.Mlon(i)); % calculates the distance between two events given the

lat and lon of the stations and the bouncepoint if dist < 95 %ray paths reflected at less than 95 degrees

are considered only MIDPT=[]; MIDPT(1)=SNINFO.Mlat(i); MIDPT(2)=SNINFO.Mlon(i);

[Ptime,Praypar]=timetool('P',[],'iasp91',SNINFO.EVinfo(i,:),[MIDPT

0]); %outputs the time and corresponding ray parameter for

the event using the IASPI91 model, event location and

midpoint/boucepoint depth. %If the bouncepoint is deeper than 10km, this line

automatically sets the bouncepoint elevation to the surface at 0km Praypar=Praypar(1)/6371; %flat earth conversion else Praypar=0.04;


107

end

[Synth]=runRTreflectionWD(abs(SNINFO.MZ(i)),Praypar,1/1000,(len_data*

dt-10),0.001); %creates the synthetic seismogram for removing "ghost"

reflections in the PdP data caused by the ocean seismogramTH=GFILT_data_2015(Synth.seismogram,9.0,[5

5],Synth.dt,'l'); %creates the theoretical filtered seismogram for

PdP raypaths reflecting off of the ocean bottom %at 9 Hz Time1000=[(1/1000):1/1000:(len_data*dt)]; %interpolated

time for every 0.001s. The time for PdP data is 289, so 299 is set

for an additional 10s at the end of the seismogram Time10=[dt:dt:(len_data*dt)];% same as above, but

interpolating for every 0.01s seismogramTH=interp1(Time1000,seismogramTH,Time10);%Sets

the sample rate to 10 samples/s ishift=round(Synth.shift*(1/dt)); %shifts data over 10s

for Tpad

% figure(1) % plot(seismogramTH,'b') % hold on seismogramTH=[seismogramTH(ishift:end)

seismogramTH(1:ishift-1) ]; % figure (3) % plot(seismogramTH,'r') % grid on % pause % hold off for ch=11:18 data(ch)=[] end

for ch=1:8 Do=data(ch).data; [nrow,ncol]=size(Do); if ncol>nrow Do=Do'; end null_check=isempty(Do);

% figure(1) % plot(Do) % title(num2str(SNINFO.MZ(i))) % grid on

if null_check==0 if length(Do)<3001 Do=[Do;0]; end fftDo=fft(Do);% fast fourier transform to the

frequency domain(raw data) fftSynth=fft(seismogramTH)'; TopDo=fftDo.*conj(fftSynth);%numerator for

deconvolution, removing ocean bottom reflection effect BotSynth=fftSynth.*conj(fftSynth);


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%max(abs(BotSynth))*WL; % scaling the maximum

amplitude for the ocean bottom synthetic waveform down iWL=find(BotSynth<max(BotSynth)*WL); % scaling

all other amplitudes BotSynth(iWL)=max(BotSynth)*WL; %finding maximum

amplitudes

Do_ST=real(ifft(fftDo./BotSynth)); Do_S=GFILT_data_2015(Do_ST,4.0,[5 5],1/20,'l'); data(ch+10).data=Do_S; % figure(2) % plot(data(ch+10).data) %

title(num2str(SNINFO.MZ(i))) % grid % hi='hi inner' % pause end end save(Fn,'data') end end end

end

function

[time,raypar]=TIMETOOL(phase,d,model,event,receiver,curvefilename) %MatTimes Tool: % %TIMETOOL Calculate seismic travel time(s) and ray parameter(s) for

a given phase. % 1)

[TIME,RAYPAR]=TIMETOOL(PHASE,VARIABLE,MODEL,EVENT,RECEIVER,CURVEFILEN

AME) % Compute travel time(s) and ray parameter(s) for the desired

PHASE/MODEL pair based % on the arc distance computed from EVENT and RECEIVER. Travel

time curves % for all possible ranges as well as input and output are computed

and saved % to CURVEFILENAME. If CURVEFILENAME is left off, a curve file is

still saved % to the Curves directory under the name: PHASE_MODEL_CURVES.MAT

(where PHASE and % MODEL are variable. PHASE is a string naming an existing phase

(i.e., 'SKKS') % as generated using MKPHASE. VARIABLE refers to the depth(s) of

the variable as % designated in MKPHASE. In the case that the input phase does not

have a variable, % the input should be set to an empty vector ([]). MODEL is a

string referring to


109

% a 1D spherically-symmetric Earth velocity model (i.e., 'iasp91').

EVENT and % RECEIVER are the earthquake and receiver locations, respectively.

If % length(EVENT)=2, EVENT(1) must be the arc distance in degrees

while EVENT(2) must % be the source depth in km. With this input type, RECEIVER must

be a scalar % representing the seismic station elevation in km. If

length(EVENT)=3, EVENT(1:2) % must be the earthquake latitude and longitude while the third

element is the % earthquake depth. With this input, RECEIVER must be three

elements as well. % RECEIVER(1:2) must be the station latitude and longitude while

the third element % is the station elevation in km. Note that the latter input is

necessary for % mapping a phase using MAPdPHASE. % % 2)

[TIME,RAYPAR]=TIMETOOL(PHASE,VARIABLE,MODEL,DATAFILE,CURVEFILENAME) % Extracts event and receiver information from a data file,

calculates travel time % and ray parameter then saves travel time curves to a curvefile.

DATAFILE is a % string argument naming a structured array data file generated by

SACTOOL % (importation from Seismic Analysis Code). New fields are set to

the "data" in % DATAFILE. CURVEFILENAME is the same as above. % % % NOTE: RAYPAR is the ray parameter in radiians/second, angular ray

parameter based % on the Earth flattening transformation (i.e., (sec/km)*r0 where

r0 is the radius % of the Earth). % % EXAMPLES: % % [time,raypar]=timetool('P',[],'iasp91',[35 0],[0]) % [time,raypar]=timetool('P',[],'iasp91~slowiasp91',[35 0],[0])

%two-ended model % [time,raypar]=timetool('P',[],'iasp91',[35 0],[0],'temp') % [time,raypar]=timetool('P',[],'iasp91',[35 -112 35],[2 -110

0.5]) % [time,raypar]=timetool('Pds',[0:20:660],'iasp91',[38 -114

35],[2 -110 0.5]) % [time,raypar]=timetool('PKKP',[],'iasp91',[110 0],0)% % [time,raypar]=timetool('Pds',[660],'iasp91','event0920z.mat') % To see structured array for this kind of input, type: % % load event0920z.mat % data(1) % % To extract time and ray parameters, type: % % time=data(1).Pds_iasp91_time % raypar=data(1).Pds_iasp91_rayp %


110

% %disp(['TIMETOOL...Working']) load dir_path.mat %path infor.

opt='n'; %initial option %condition input model=rmmat(model); if nargin==5 & isstr(receiver) receiver=rmmat(receiver); cname=[receiver '_curves.mat']; elseif nargin==5 & isstr(receiver)==0 cname=[phase '_' model '_curves.mat']; elseif nargin==6 curvefilename=rmmat(curvefilename); cname=[curvefilename '_curves.mat']; elseif nargin==4 event=rmmat(event); cname=[event '_' phase '_' model '_curves.mat']; end

if isstr(event)%Earthquake and receiver info from file opt='file'; datafile=event; %avoid unix problems with ".mat" extension datafile=rmmat(datafile); %load in data file and store variable name s=load([datafile '.mat']); load([datafile '.mat']); abcd=[];ijk=0; %determine variable list in structured array while length(abcd)==0 ijk=ijk+1; dff=fieldnames(s(ijk)); dff=(char(dff(ijk))); df=eval(dff); abc=char(fieldnames(df)); %%%%%%%%%%%%%% %abc=char(df); abcd=strmatch('gcarc',abc); end %define event and reciever pair event=[data(1).event_lat data(1).event_lon data(1).event_depth]; abcd=strmatch('borehole_depth',abc); if length(abcd)==0 receiver=[data(1).station_lat data(1).station_lon

data(1).station_elevation-data(1).station_depth]; else receiver=[data(1).station_lat data(1).station_lon

data(1).station_elevation-data(1).borehole_depth]; end end

if nargin>4 & isstr(receiver) %data from curve file mkcurve='yes'; curvefile=receiver; elseif nargin==4 mkcurve='no'; else mkcurve='no'; end


111

%see if library file exists ddd=dir([wherelibfiles '/' phase '_' model '_lib.mat']); if length(ddd)==0 disp([' ']); disp(['No Lib File Associated With the Input Phase and Model: '

phase '_' model '.mat']); disp([' ']); disp(['Creating Library File']); phasetool(phase,model); end

load([wherelibfiles '/' phase '_' model '_lib.mat']);

if length(event)==2 range=event(1); sourced=event(2); receiver_elev=receiver; elseif length(event)==3 & opt(1)~='f' a=6378.140; b=6356.752; e=sqrt(a^2-b^2)/a; sma=a; geoid=[sma/6371 e]; %mapping utility to find great circle arc

range=distance(receiver(1),receiver(2),event(1),event(2),geoid)*180/p

i; sourced=event(3); receiver_elev=receiver(3); elseif length(event)==3 & opt(1)=='f' range=data(1).gcarc; % all OK sourced=event(3); receiver_elev=receiver(3); else event; receiver; disp('You must enter valid "event" and/or "receiver" variables'); end depths=lib.depths(:); d=d(:); ld=length(d); if ld==0 ld=1; end

ldepths=length(lib.depths); %source and receiver ranges receiverrange=interp2(lib.elev,lib.rayp,lib.rec_range,receiver_elev,l

ib.rayp); sourcerange=interp2(lib.sdepths,lib.rayp,lib.source_range,sourced,lib

.rayp); %source and receiver times receivertime=interp2(lib.elev,lib.rayp,lib.rec_time,receiver_elev,lib

.rayp); sourcetime=interp2(lib.sdepths,lib.rayp,lib.source_time,sourced,lib.r

ayp); %source and receiver tau receivertau=interp2(lib.elev,lib.rayp,lib.rec_tau,receiver_elev,lib.r

ayp);


112

sourcetau=interp2(lib.sdepths,lib.rayp,lib.source_tau,sourced,lib.ray

p);

phase_range=lib.ph_range+(receiverrange+sourcerange)*ones(1,ldepths); phase_tau=lib.ph_tau+(receivertau+sourcetau)*ones(1,ldepths); phase_time=lib.ph_time+(receivertime+sourcetime)*ones(1,ldepths);

if length(lib.depths)>1 phase_range=interp2(lib.depths,lib.rayp,phase_range,d',lib.rayp); phase_time=interp2(lib.depths,lib.rayp,phase_time,d',lib.rayp); phase_tau=interp2(lib.depths,lib.rayp,phase_tau,d',lib.rayp); end

pr=phase_range*180/pi;

pr(pr>180)=abs(360-pr(pr>180)); raypar=zeros(ld,10); time=zeros(ld,10); if range==180 range2=180-1e-5; else range2=range; end

for j=1:ld b=pr(:,j); a=b-range2; x=a(abs(a)<90); y=lib.rayp(abs(a)<90);

lx=length(x); if lx>0 X=[x(1:lx-1) x(2:lx)]; Y=[y(1:lx-1) y(2:lx)]; [X,jj]=sort(X,2); for k = 1:lx-1, Y(k,:) = Y(k,jj(k,:)); end row=find(X(:,1)<=0 & X(:,2)>=0); lrow=length(row); X=X(row,1:2); Y=Y(row,1:2); else lrow=0; end lrow=lrow; max(pr(:,j)); min(pr(:,j)); rang2=range2;

if lrow>0 & range2<=max(pr(:,j)) & range2>=min(pr(:,j)) clear aa; for i=1:lrow aa(i)=interp1q([X(i,:)]',[Y(i,:)]',0); end kk=find(isnan(aa)~=1); if length(kk)>0 raypar(j,1:length(kk))=aa(kk); %fix reflection problem if

isempty(strmatch(lib.DeepestIncidence,'Reflection'))==0 ... & length(lib.depths)>1


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[pturn,sturn]=turntool(model,raypar(j,:),lib.shad); if lower(lib.WaveType)=='p'; ff=find(pturn>d(j)==1); else ff=find(sturn>d(j)==1); end raypar(j,1:length(ff))=raypar(j,ff); raypar(j,length(ff)+1:length(kk))=nan; end

else raypar(j,1:length(kk))=nan; end else raypar(j,1)=nan;

end if range2(1)~=0 rayrow=raypar(j,:); raypf=[rayrow(find(rayrow~=0))]; else rayrow=raypar(j,:); raypf=0; end lrf=length(raypf);

time(j,1:lrf)=interp1(lib.rayp,phase_time(:,j),raypf); end %build matrices raypar(isnan(raypar))=0; if length(d)>1 n=find(sum(raypar(:,2:10))==0)+1; raypar(:,n)=[]; time(:,n)=[]; else n=find(raypar(2:10)==0)+1; raypar(n)=[]; time(n)=[]; end raypar(isnan(time))=nan;

if nargin==4 %data(1)=setfield(data(1),'name','MatTimes data(1)'); for jk=1:length(data) dtime=data(jk).begintime-data(jk).evtime; dtime=sum(dtime.*[31536000 86400 3600 60 1]);%convert to

seconds time2=time-dtime; data=setfield(data,[1,jk],'event',event); data=setfield(data,[1,jk],'receiver',receiver); %disp(['New fields set in variable "data" in file: ' datafile

'.mat']); %disp([' ']); data=setfield(data,[1,jk],[phase '_' model '_dpth'],d); %disp([phase '_' model '_dpth']); data=setfield(data,[1,jk],[phase '_' model '_time'],time2); %disp([phase '_' model '_time']); data=setfield(data,[1,jk],[phase '_' model '_rayp'],raypar); %disp([phase '_' model '_rayp']); %disp([' ']);


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end save([datafile '.mat'],'data','-append') else curves.times=phase_time; curves.tau=phase_tau; curves.ranges=pr; output.time=time; output.raypar=raypar; input.phase=phase; input.depths=d; input.model=model; input.event=event; input.receiver=receiver; if cname(1)~='/'; save([wherecurvefiles '/' cname],'curves','input','output') %disp(['New curve file created: ' [wherecurvefiles '/'

cname]]); else save([cname],'curves','input','output') %disp(['New curve file created: ' [cname]]); end

end

function [Synthetic]=runRTreflectionWD(ZW,p,dt,tlen,tol) % Tpad) % [Time,seismogram]=runRTreflectionPQ2(50,1/1000,0.06,0.0001,'r'); % ran in 21 second on my laptop % [Time,seismogram]=runRTreflectionPQ2(50,1/1000,0.06,0.0005,'b'); % ran in 4 seconds on my laptop % [Time,seismogram]=runRTreflectionPQ2(50,1/1000,0.06,0.001,'g'); % ran in 1.6 sec on ,y laptop %tlen = trace length

nsamples=round(tlen/dt); seismogram=zeros(1,nsamples);

Vp=[ .343 1.5 2.0 3.7 5.3 7 ]; Vs=[ 10e-90 10e-80 1 [3.7 5.3 7 ]./1.8 ]; den=[0.00128 1 1.7 2.3 2.75 2.95 ]; z=[ ZW .4 .5 1.8 ]; ZW=ZW; ipnew=0; lz=length(z); for a=1:lz path1=[lz+1 a]; path2=[a lz+1]; [tt1,amp1] = RTreflectionPWD(Vp,den,z,path1,Vs,p,1,0); [tt2,amp2] = RTreflectionPWD(Vp,den,z,path2,Vs,p,1,1); PR(1,a).path1(1,:)=path1; PR(1,a).tt1(1)=tt1; PR(1,a).amp1(1)=amp1; PR(1,a).path2(1,:)=path2; PR(1,a).tt2(1)=tt2; PR(1,a).amp2(1)=amp2;


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PR(1,a).ipr(1)=1; path=[PR(1,a).path1(1,:) ]; tt=PR(1,a).tt1(1)+PR(1,a).tt2(1); amp=PR(1,a).amp1(1)*PR(1,a).amp2(1); if tt < tlen nsample=round(tt/dt); seismogram(nsample)=seismogram(nsample)+amp; if abs(amp)>tol ipnew=ipnew+1; path_temp(ipnew,:)=path; amp_temp(ipnew)=amp; tt_temp(ipnew)=tt; a_temp(ipnew)=a; end end if a==1 Tdirect=tt1+tt2; end end

for a=1:lz ia=find(a_temp==a); PR(1,a).path1=path_temp(ia,:); PR(1,a).tt1=tt_temp(ia)-PR(1,a).tt2(1); PR(1,a).amp1=amp_temp(ia)/PR(1,a).amp2(1); PR(1,a).ipr(1)=length(ia); end go_on=length(tt_temp); path_temp=[]; tt_temp=[]; amp_temp=[]; a_temp=[];

for a=1:lz-1 iabc=0; for b=a+1:lz for c=1:b-1 path=[a b c]; [tt,amp] = RTreflectionPWD(Vp,den,z,path,Vs,p,0,0); iabc=iabc+1; ABC(a).paths(iabc,:)=path; ABC(a).tt(iabc)=tt; ABC(a).amp(iabc)=amp; ABC(a).c(iabc)=c; end end ABC(a).iabc=iabc; end go_on=10; iw=0;

while go_on >= 1 iw=iw+1; ipnew=0; for a=1:lz-1 for ipr=1:PR(iw,a).ipr for iabc=1:ABC(a).iabc path=[PR(iw,a).path1(ipr,:) ABC(a).paths(iabc,2:end)

];

tt=PR(iw,a).tt1(ipr)+ABC(a).tt(iabc)+PR(1,ABC(a).c(iabc)).tt2(1);

amp=PR(iw,a).amp1(ipr)*ABC(a).amp(iabc)*PR(1,ABC(a).c(iabc)).amp2(1);


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if tt < tlen nsample=round(tt/dt); seismogram(nsample)=seismogram(nsample)+amp; if abs(amp)>tol ipnew=ipnew+1; path_temp(ipnew,:)=path; amp_temp(ipnew)=amp; tt_temp(ipnew)=tt; a_temp(ipnew)=ABC(a).c(iabc); end end end end end for a=1:lz ia=find(a_temp==a); PR(iw+1,a).path1=path_temp(ia,:); PR(iw+1,a).tt1=tt_temp(ia)-PR(1,a).tt2(1); PR(iw+1,a).amp1=amp_temp(ia)/PR(1,a).amp2(1); PR(iw+1,a).ipr(1)=length(ia); end go_on=length(tt_temp); path_temp=[]; tt_temp=[]; amp_temp=[]; a_temp=[]; end

seismogram=[zeros(1,round(10/dt)) seismogram]; %if i uncomment these

ad Tpad to input Time=[1:length(seismogram)]*dt; % Time=Time-Tdirect+Tpad; shift=Tdirect+10; % plot(Time,seismogram,ccc,'linewidth',2)

Synthetic.seismogram=seismogram; Synthetic.Time=Time; %Synthetic.Tdirect=Tdirect; Synthetic.Vp=Vp; Synthetic.Vs=Vs; Synthetic.den=den; Synthetic.z=z; Synthetic.PR=PR; Synthetic.dt=dt; Synthetic.shift=shift;

Beaming

Beamed data is created with the aid of “PdP_beamer_EV_12_19_2019” and the sub-routine

“beamev_2019_11_22_2019”.

function PdP_beamer_EV_12_19_2019(inD,outD,len_data) %inD=input directory %outD=output directory %lendata=? % % beam PdP or SdS data % load('SNINFO_good_SdS.mat')


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% warning('off') % % killer='noo'; home0=pwd; %Saves current working directory as home0 num_high=0; first_recording_in_event=0; saver=0; if exist('istart_beamer_PdP.mat')==2 load('istart_beamer_PdP.mat') end keep=length(SNINFO.keep); first_recording_in_event=first_recording_in_event; while first_recording_in_event<length(SNINFO.keep) save('istart_beamer_PdP.mat','first_recording_in_event') saver=saver+1 first_recording_in_event=first_recording_in_event+1; Fn=SNINFO.fileout(first_recording_in_event).name; Fn(1)=inD; Fn(4)='T'; jdir=findstr(Fn,'/FD'); in_event=1; last_recording_in_event=first_recording_in_event+in_event-1; fn2=SNINFO.fileout(last_recording_in_event).name; fn2(1)=inD; fn2(4)='T';

%the while loop above goes through all files in SNINFO_PdP_GOOD. %first_recording_in_event = a counter used to loop through files %Fn = the name of the first file to be used in the beaming

process %jdir = the index of '/FD' and makes it possible to select an

event %folder and beam data from the same event %fn2 = the name of the second file to be used in the beaming

process

while Fn(1:jdir) == fn2(1:jdir) load(fn2) dt=data.dt; T0=data.SACT0; w1=floor((T0-20)/dt); w2=ceil((T0+60)/dt); Dt=data(1).data; [icol,irow]=size(Dt); %%%Row before column time=[0:(length(Dt)-1)]*dt; if icol==1, Dt=Dt'; end if length(Dt)<len_data, Dt=[Dt;0]; end Dft=GFILT_data_2015(Dt,0.06,[5 5],dt,'l'); Df(:,in_event)=(1/std(Dft(floor(w1/2):w1)))*Dft; Do(:,in_event)=(1/std(Dt(floor(w1/2):w1)))*Dt;

% plot(Dft) % title('Dft') % pause % Df(:,iFn)=(1/max(Dft(w1:w2)))*Dft; % Do(:,iFn)=(1/max(Dt(floor(w1/2):w1)))*Dt; if length(data) == 11 Dt11=data(11).data;


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[icol,irow]=size(Dt11); if icol==1, Dt11=Dt11'; end if length(Dt11)<len_data, Dt11=[Dt11;0]; end Dft11=GFILT_data_2015(Dt11,0.06,[5 5],dt,'l'); Df11(:,in_event)=(1/std(Dft11(floor(w1/2):w1)))*Dft11;

%don't get this line good enough Do11(:,in_event)=(1/std(Dt11(floor(w1/2):w1)))*Dt11; else Dft11=Dft; Df11(:,in_event)=Df(:,in_event); %%%Df11 =Df Do11(:,in_event)=Do(:,in_event); end NAMES(in_event).name=fn2; EVENT(in_event,:)=SNINFO.EVinfo(in_event,:); STA(in_event,:)=SNINFO.STinfo(in_event,:); last_recording_in_event=first_recording_in_event+in_event; in_event=in_event+1; %%% switch line 76 and 77 fn2=SNINFO.fileout(last_recording_in_event).name; fn2(1)=inD; fn2(4)='T' %This while loops through recording around the world from the

same event %and filters the recording, saves NAMES, EVENT, STA for all %recordings as columns %dt=sample rate of recording %Dt=recording in time and amplitude %Dft=filtered recording %Df=normalized low pass filtered recording %Do=normalized unfiltered recording end

%The while loop above is exited when Fn(1:jdir) == fn2(1:jdir),

which %simply means Fn and fn2 are not in the same event folder taper_win=0*Dft; taper_win(w1:w2)=1; in_event=in_event-1; R_degree=[1 2 4 8 16 32 360];

%taper_win=matrix to taper Dft %R_degree=different beaming radii

for k=1:in_event load(NAMES(k).name)

[ARCLENm,AZm]=distance(STA(k,1),STA(k,2),EVENT(k,1),EVENT(k,2)); [Mlat,Mlon]=reckon(STA(k,1),STA(k,2),0.5*ARCLENm, AZm);

%midpoint between what and what? data(1).Mlat=Mlat; data(1).Mlon=Mlon; data(1).in_rad=1; %why is data.inrad always 1? name_out=NAMES(k).name; name_out(1)=outD; name_out(4)='D'; SNINFO.Mlat(k)=Mlat; SNINFO.Mlon(k)=Mlon; numd=findstr(name_out,'/'); newdir=name_out(1:numd);


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%the above for statement loops through recordings from the

same %event from 1 to in_event -holds the total number of events

in the %same event folder. %[ARCLENm, AZMm]=stores the arc length and azimuth of all %recordings %[Mlat, Mlon]=stores the midpoint between.... %name_out=prepares a variable to create a new directory to

store %beamed data if k==1, mkdir(newdir), end for iR_degrees=1:length(R_degree) R_degree_now=R_degree(iR_degrees); i2=iR_degrees+1; k_ev=first_recording_in_event+k-1; %%%we don't use this

variable if in_event > 1

[B,in_rad]=beamev_2019_11_22_2019(Df(:,k),STA(k,:),STA,Df,Do,R_degree

_now,dt,taper_win); if length(Df11) > 2

[B11,in_rad]=beamev_2019_11_22_2019(Df11(:,k),STA(k,:),STA,Df11,Do11,

R_degree_now,dt,taper_win); end else in_rad=0; end %The for loop above goes through an event folder and

beams each %recordings with other recordings of the same event using %different beaming radii (R_degree) %iR_degrees=keeps count of the number of beaming radii to

be %used and when %beamev calls on beamev to beam the recordings in the

event %folder if in_rad>1.5 data(i2)=data(1); data(i2).data=B; data(i2).in_rad=in_rad; SNINFO.in_rad(k_ev,i2-1)=in_rad; if exist('B11')==1 data(i2+10)=data(i2); data(i2+10).data=B11; end

else data(i2)=data(1); data(i2).data=[]; data(i2).in_rad=in_rad;

data(i2+10)=data(i2); data(i2+10).data=[]; end end save(name_out,'data') end


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if saver/500==round(saver/500),

save('SNINFO_PdP_GOOD.mat','SNINFO'), end first_recording_in_event=first_recording_in_event+in_event-1; end save('SNINFO_PdP_GOOD_TRANS.mat','SNINFO') end

function

[B1,num_in_rad]=beamev_2019_11_22_2019(Df_center,STA_center,STA,Df,Do

,R_degrees,dt,taper_win) %Df_center=filtered reference recording %STA_center=station info of reference recording %STA=station info of recordings to be beamed %Df=filtered recordings to be beamed %Do=unfiltered recordings to be beamed %dt=sample rate %taper_win=taper window

% % a=dir('RF_8*') % % figure(1) [ARCLEN,AZ]=distance(STA_center(1),STA_center(2),STA(:,1),STA(:,2)); in_rad=find(ARCLEN<=R_degrees); if length(in_rad) > 1.5 Df=Df(:,in_rad); Do=Do(:,in_rad); % for i=1:length(a) % % % load(a(i).name) % data=GFILT_struc_2014(data,f(2),[5 5],'l'); % data=GFILT_struc_2014(data,f(1),[5 5],'h'); % IT1=(data(3).SACT0-3)/data(3).dt; % IT2=(data(3).SACT0+10)/data(3).dt; % n=max(data(3).data(IT1:IT2)); % T=([1:length(data(3).data)]*data(3).dt)-data(3).dt; % plot(T,0.5*data(3).data/n+i,'k'); % hold on % D(:,i)=data(3).data; % end % axis([0 90 0 i+1]) % hold off % [x y]=ginput(2); % ix1=round(x(1)/data(3).dt); % ix2=round(x(2)/data(3).dt); % iy=round(mean(y)); % R=flipud(D(ix1:ix2,iy)); % BD=D(ix1:ix2,:); % color='rbmcgrbmcgrbmcgrbmcg'

Df_center_flipped=flipud(Df_center.*taper_win);

ac=conv(Df_center.*taper_win,Df_center_flipped); % plot(ac) % title('ac') [junk,lag_ac]=max(abs(ac)); for i=1:length(in_rad)


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%cc=conv(BD(:,i),R); cc=conv(Df(:,i).*taper_win,Df_center_flipped); % plot(cc) % title('cc') % pause [junk,lag_cc]=max(abs(cc)); %%%Donot get line LAG_CC(i)=lag_cc-lag_ac; cc_real=cc(lag_cc); if cc_real < 0 sign_fixer(i)=-1; else sign_fixer(i)=1; end

end

T=[1:length(Df_center)]*dt; % plot(T,Df_center,'k','linewidth',2) % hold on for i=1:length(in_rad) %load(a(i).name) if LAG_CC(i) ==0 Do_shifted(:,i)=Do(:,i); Df_shifted(:,i)=Df(:,i); elseif LAG_CC(i)<0 shifter=abs(LAG_CC(i)); Do_shifted(:,i)=[zeros(shifter,1); Do(1:end-shifter,i)]; Df_shifted(:,i)=[zeros(shifter,1); Df(1:end-shifter,i)]; else shifter=LAG_CC(i); Do_shifted(:,i)=[Do((shifter+1):end,i) ;

zeros(shifter,1)]; Df_shifted(:,i)=[Df((shifter+1):end,i) ;

zeros(shifter,1)]; end Df_shifted(:,i)=sign_fixer(i)*Df_shifted(:,i); Do_shifted(:,i)=sign_fixer(i)*Do_shifted(:,i); % hold off % figure(1) % plot(T,Df_center,'m','linewidth',4); % hold on % plot(T,Do_center,'k','linewidth',2); % plot(T,Df(:,i),'g','linewidth',4); % plot(T,Df_shifted(:,i),'r','linewidth',2); % plot(T,Do_shifted(:,i),'b','linewidth',2); % length(length(in_rad)) % grid on % axis([300 450 1.5*min(Df_center) 1.5*max(Df_center)]) % pause end B1=mean(Do_shifted,2); %%%Why do we stack only the original data % plot(T,B1,'k','linewidth',2) % hold off %name=a(iy).name; num_in_rad=length(in_rad); %save(name,'data') else B1=[]; num_in_rad=1; end


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Codes for Ray Tracing

ADD1DPdPinfo_PdP_2019 and ADD1DSdSinfo_PdP_2019 ray traces all seismic source

receiver pairs.

function ADD1DPdPinfo_PdP_2019(Disk) %This code finds the travel time and depths for each of seismic

source %and receiver pair for PdP load('ZPDP_GC.mat') load('SNINFO_PdP_GOOD.mat')

%for k=length(PATH):-1:1

if exist('IS_startPdP.mat')==2 load('IS_startPdP.mat') IS=k else IS=1 end

for k=IS:1:length(SNINFO.keep) fname=[Disk SNINFO.fileout(k).name(2:end)]; fname(4)='D' SNINFO.beamed(k)=0; if exist(fname)==2 SNINFO.beamed(k)=1; load(fname) data(10)=data(1);

[arcd,AZm]=distance(SNINFO.EVinfo(k,1),SNINFO.EVinfo(k,2),SNINFO.STin

fo(k,1),SNINFO.STinfo(k,2));

[MlatT,MlonT]=reckon(SNINFO.EVinfo(k,1),SNINFO.EVinfo(k,2),0.5*arcd,

AZm); SNINFO.GCA(k)=arcd; eventdepth=round(SNINFO.EVinfo(k,3)/5)*5; %We don't have EVZ

%[arcd,MidT,MidD]=findMidPointTD(EVLAT(k),EVLON(k),STLAT(k),STLON(k),

EVZ(k),1000,'PdP'); midpointdepth=1000; %where do we get this from? arcd=round(arcd,1); if arcd>=71; farcd=ZPDP_GC_Z_D_GC(1,1,:)-arcd; [junk,ifarcd]=min(farcd); igc=find(abs(farcd)<0.001); ied=(eventdepth/5+1); imd=(midpointdepth/5+1); MidT=ZPDP_T_Z_D_GC(ied,[1:imd],igc); MidD=ZPDP_D_Z_D_GC(ied,[1:imd],igc); data(10).zz1=MidD; data(10).dtt1=abs(MidT-MidT(1)); SNINFO.Mlat(k)=MlatT; SNINFO.Mlon(k)=MlonT;


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data(10).data=[]; data(10).mlat1=squeeze(MlatT); data(10).mlon1=squeeze(MlonT); SNINFO.Lzz1(k)=length(MidD); save(fname,'data') end if round(k/5000) == k/5000, save('IS_startPdP.mat','k') save('SNINFO_good_PdP.mat','SNINFO') k=k hi='saved k' end end end clear k fname GCA az arcd MidT MidD arcd MidT MidD clear ZPDP_T_Z_D_GC ZPDP_D_Z_D_GC ZPDP_GC_Z_D_GC ZPDP_RP_Z_D_GC

ZPDP_T_Z_D_GC ZPDP_Z_Z_D_GC save('SNINFO_good_PdP.mat','SNINFO') end

function ADD1DSdSinfo_SdS_2019(Disk) %This code finds the travel time and depths for each of seismic

source %and receiver pair for SdS load('ZSDS_GC.mat') load('SNINFO_SdS_GOOD.mat')

%for k=length(PATH):-1:1

if exist('IS_startSdS.mat')==2 load('IS_startSdS.mat') IS=k else IS=1 end

for k=IS:1:length(SNINFO.keep) fname=[Disk SNINFO.fileout(k).name(2:end)]; fname(4)='B' SNINFO.beamed(k)=0; if exist(fname)==2 SNINFO.beamed(k)=1; load(fname) data(10)=data(1);

[arcd,AZm]=distance(SNINFO.EVinfo(k,1),SNINFO.EVinfo(k,2),SNINFO.STin

fo(k,1),SNINFO.STinfo(k,2));

[MlatT,MlonT]=reckon(SNINFO.EVinfo(k,1),SNINFO.EVinfo(k,2),0.5*arcd,

AZm); SNINFO.GCA(k)=arcd; eventdepth=round(SNINFO.EVinfo(k,3)/5)*5; %We don't have EVZ

%[arcd,MidT,MidD]=findMidPointTD(EVLAT(k),EVLON(k),STLAT(k),STLON(k),

EVZ(k),1000,'PdP'); midpointdepth=1000; %where do we get this from? arcd=round(arcd,1);


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if arcd>=71 && arcd<=179.99999999999999999999999999999999999; farcd=ZSDS_GC_Z_D_GC(1,1,:)-arcd; [junk,ifarcd]=min(farcd); igc=find(abs(farcd)<0.001); ied=(eventdepth/5+1); imd=(midpointdepth/5+1); MidT=ZSDS_T_Z_D_GC(ied,[1:imd],igc); MidD=ZSDS_D_Z_D_GC(ied,[1:imd],igc); data(10).zz1=MidD; data(10).dtt1=abs(MidT-MidT(1)); SNINFO.Mlat(k)=MlatT; SNINFO.Mlon(k)=MlonT; data(10).data=[]; data(10).mlat1=squeeze(MlatT); data(10).mlon1=squeeze(MlonT); SNINFO.Lzz1(k)=length(MidD); save(fname,'data') end if round(k/1000) == k/5000, save('IS_startSdS.mat','k') save('SNINFO_good_SdS.mat','SNINFO') k=k hi='saved k' end end end clear k fname GCA az arcd MidT MidD arcd MidT MidD clear ZPDP_T_Z_D_GC ZPDP_D_Z_D_GC ZPDP_GC_Z_D_GC ZPDP_RP_Z_D_GC

ZPDP_T_Z_D_GC ZPDP_Z_Z_D_GC save('SNINFO_good_SdS.mat','SNINFO') end

Deconvolution and Stacking Codes

Three codes work in tandem to perform wavefield iteration deconvolution namely

“loc_decon_call_JH_2020_PdP(SdS)”,

“PdP(SdS)_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2”

, and “IT_DECON_JH_CC_WID_r_p2_John_edit”. “loc_decon_call_JH_2020_PdP(SdS)”

loops through several midpoints and calls

“PdP(SdS)_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2” which in turn calls

“IT_DECON_JH_CC_WID_r_p2_John_edit” to perform deconvolution on traces in the given

midpoint gather. To assist the programs for deconvolution “Taperfilt_JH_2020_endtaper” is

used to create the signal and source function.

% %This code calls on PdP_CC_2020real_SRC_from_SIG_WID_depth to

perform WID on % %different varying latitudes and longitudes % %lats and lons constraints the area where % %deconvolution.


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% %PdPFZ_common(:,n,m) contains stacked in time,depth converted PdP

functions % %PdPFZ_stack(:,n,m) contains depth converted, stacked in depth PdP

functions % %PdPFZ_table(:,n,m) contains deconvolution and stacking info

% %PdPFZ_common2(:,n,m) contains stacked in time,depth converted PdP

functions for un-deoceaned data % %PdPFZ_stack2(:,n,m) contains depth converted, stacked in depth PdP

functions for un-deoceaned data % %PdPFZ_table2(:,n,m) contains deconvolution and stacking info for

un-deoceaned data

lats=[34:1:66]; lons=[160:1:210]; %Aleutian %lats=[20:2:26]; %lons=[-160:2:-150]; %Hawaii %lats=[34:2:38]; %lons=[160:2:164]; %Aleutian

src_w1=[-10]; %margin before T0 for the source. For T0 200s and

src_w1=-10 then source begins at 190s. src_w2=[40]; %margin after T0 for the source. For T0 200s and

src_w2=40 then source ends at 240s sig_w1=[-160]; %margin before start of signal in time. Early times

correspond with deeper depths. For T0 200s and sig_w1=-160, signal

begins at 40s sig_w2=[0]; %margin after end of signal in time. Later times

correspond with shallower depths. For T0 200s and sig_w2=-50, signal

ends at 150s % %NB: T0 is the arrival of the PdP/SdS

rolloff=[10]; %rolloff is th gradual decrease of amplitude power to

0. from the start to end of a taper margin sig_channel=[4]; src_channel=[8]; FREQ=[0.1]; %frequency at which source and signal is low pass

filtered num_it=[80]; taper_sig=[0];

fname=['John_edit_PdP_alu_numit_'];

for taper_i=1:length(taper_sig) for it=1:length(num_it) for ch_i=1:length(src_channel) for roll_i=1:length(rolloff) for freq_i=1:length(FREQ)

n=0; for mlat=lats n=n+1; m=0; for mlon=lons m=m+1;


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if mlon>180 mlon=-360+mlon; %The if statement

converts longitudes from a -180 to 180 scale to a 0 to 360 scale end

[PdPFZ_common(:,n,m),PdPFZ_stack(:,n,m),traces(:,n,m),PdP_table(n,m)]

= PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO,mlat, mlon,

1.5, 18, 14, 'H',

'D',rolloff(roll_i),num_it(it),0.2,4,3001,FREQ(freq_i),src_w1,src_w2,

sig_w1,sig_w2,taper_sig(taper_i)); %The above line finds the PdP functions

for de-oceaned data end end

n=0; for mlat=lats n=n+1; m=0; for mlon=lons m=m+1; if mlon>180 mlon=-360+mlon; %The if statement

converts longitudes from a -180 to 180 scale to a 0 to 360 scale end

[PdPFZ_common2(:,n,m),PdPFZ_stack2(:,n,m),traces2(:,n,m),PdP_table2(n

,m)] = PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO,mlat,

mlon, 1.5, 8, 4, 'H',

'D',rolloff(roll_i),num_it(it),0.2,4,3001,FREQ(freq_i),src_w1,src_w2,

sig_w1,sig_w2,taper_sig(taper_i)); %The above line finds the PdP functions

for regular data end end fname_out=[fname num2str(num_it(it)) '_t_sig_'

num2str(taper_sig(taper_i)) 'sig_ch_' num2str(sig_channel(ch_i))

'src_ch_' num2str(src_channel(ch_i)) '_rolloff_'

num2str(rolloff(roll_i)) '__freq_' num2str(FREQ(freq_i)) '.mat'];

save(fname_out,'lats','lons','PdPFZ_common','PdPFZ_stack','traces','P

dPFZ_common2','PdPFZ_stack2','traces2','PdP_table','PdP_table2') end fname_out=[fname num2str(num_it(it)) '_t_sig_'













127









dPFZ_common2','PdPFZ_stack2','traces2','PdP_table','PdP_table2') end

function [ synth_now_stack_z,PdP_now_stack_z,traces,PdPF_phase_info ]

= PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO, mlat,

mlon, search_rad, source_ch, sig_ch, inD, process_stage, rolloff,

num_it

,LPF,Sig2Noise,len_data,FREQ,src_w1,src_w2,sig_w1,sig_w2,taper_sig) %PdP_SdS_CC_2020 takes a midpoint and a stack radius and stacks the

events %within the stack radius. It performs a cross correlation

deconvolution

%The input arguments of

PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2 are: %mlat=latitude of stacking midpoint %mlon=longitude of stacking midpoint %search_rad=radius about the midpoint where event and station pairs

are %stacked %source_ch=channel of the source %len_data=length of data %inD=root directory of data %process_stage=F for filtered, S for signal/noise, B for beamed, D

for %debugged and beamed

%The outputs of PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2 are: %synth_now_stack_z=contains stacked in time,depth converted PdP

functions %PdP_now_stack_z=contains depth converted, stacked in depth PdP

functions %PdPF_phase_info=contains deconvolution and stacking info

% [ synth_now_stack_z,PdP_now_stack_z,PdPF_phase_info ] =

PdP_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO,10,-

152,0.25,8,2,'F','D',10,5,0.2,0,1);

[ARCLEN, ~] = distance(mlat, mlon, SNINFO.Mlat, SNINFO.Mlon); in_rad_i = find(ARCLEN<=search_rad); len_in_rad=length(in_rad_i); traces=len_in_rad.*ones(1001,1);


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PdPF_phase_info.mlat=mlat; PdPF_phase_info.mlon=mlon;

%Station_info = latitudes, longitudes and elevations of the %stations. %ARCLEN = distance in degrees between the given midpoint(mlat, mlon)

and all %the other stations in Station_info. %in_rad_i = holds the indices of all distances between

midpoint(mlat,mlon) %that are within(less than or equal) the search_rad.

m=0; signal_s=[];

for n=1:len_in_rad S_to_N=SNINFO.StoN_peak_low(in_rad_i(n)); % JSA_keep=SNINFO.jsa_keep(in_rad_i(n)); if S_to_N>=Sig2Noise %&& JSA_keep == 1 %Checks whether current

seismic file meets signal to noise and second QC parameters f1 = SNINFO.fileout(in_rad_i(n)).name; f1(1) = inD; f1(4) = process_stage %f11(n).name=f1 %save f11 f11 load(f1) %f1 stores the directory filenames in the search radius %f1 is edited to load the file the user wants based off the

inD and %process

[tstx,tsty]=size(data(sig_ch).data); %avoids seismic files

with empty recordings test_src=isempty(data(source_ch).data); if tstx == 0 || tsty == 0 || test_src == 1 else m=m+1; PdPF_phase_info.S(m).filename.name=f1;

PdPF_phase_info.S(m).sta_lat=data(1).station_lat; PdPF_phase_info.S(m).sta_lon=data(1).station_lon; PdPF_phase_info.S(m).ev_lat=data(1).event_lat; PdPF_phase_info.S(m).ev_lon=data(1).event_lon; PdPF_phase_info.S(m).ev_depth=data(1).event_depth; PdPF_phase_info.S(m).gcarc=data(1).gcarc;

signal_temp =

data(sig_ch).data*(1/max(abs(data(source_ch).data))); shape=size(signal_temp); if shape(2) > shape(1) signal_temp=signal_temp'; end

dt=data(1).dt; T0 = data.SACT0; dtt(:,m)=double(data(10).dtt1); dzz(:,m)=double(data(10).zz1); %signal_temp stores the data from the signal channel %dt holds the sample rate of the data %T0 = Time of first arrival


129

signal(:,m) = GFILT_data_2015(signal_temp,FREQ,[5

5],dt,'l'); source(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0, rolloff,0,src_w1); source(:,m) =

Taperfilt_JH_2020_endtaper(source(:,m),dt,T0,10*rolloff,1,src_w2); signal(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0, rolloff,0,sig_w1); if taper_sig==1 signal(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0,rolloff,1,sig_w2); end signal_s(:,m)=[signal(:,m);

zeros(round(30/data(1).dt),1)]; source_s(:,m)=[round(zeros(30/data(1).dt,1));

source(:,m)]; len_signal_s(m)=length(signal_s(:,m)); %source and signal are both tapered using the

source_taper_2020_1 %subroutine

end end end

[sigx,~]=size(signal_s);

if sigx > 0 [synth_now_stack_z,PdP_now_stack_z,PdPF_phase_info] =

IT_DECON_JH_CC_WID_r_p2_John_edit(signal_s, source_s,

num_it,dt,LPF,dzz,dtt,len_data,PdPF_phase_info); else synth_now_stack_z=zeros(1001,1); PdP_now_stack_z=zeros(1001,1); PdPF_phase_info.S='this file and other files in the radius were

empty'; end

end

% %This code calls on SdS_CC_2020real_SRC_from_SIG_WID_depth to

perform WID on % %different varying latitudes and longitudes % %lats and lons constraints the area where % %deconvolution.

% %SdSFZ_common(:,n,m) contains stacked in time,depth converted PdP

functions % %SdSFZ_stack(:,n,m) contains depth converted, stacked in depth PdP

functions % %SdSFZ_table(:,n,m) contains deconvolution and stacking info


130

lats=[34:0.5:66]; lons=[160:0.5:210]; %Aleutian %lats=[10:2:26]; %lons=[-160:2:-140]; %Hawaii

src_w1=[-20]; %margin before T0 for the source. For T0 200s and

src_w1=-10 then source begins at 190s. src_w2=[60]; %margin after T0 for the source. For T0 200s and

src_w2=40 then source ends at 240s sig_w1=[-250]; %margin before start of signal in time. Early times

correspond with deeper depths. For T0 200s and sig_w1=-160, signal

begins at 40s sig_w2=[0]; %margin after end of signal in time. Later times

correspond with shallower depths. For T0 200s and sig_w2=-50, signal

ends at 150s % %NB: T0 is the arrival of the PdP/SdS

rolloff=[10]; %rolloff is th gradual decrease of amplitude power to

0. from the start to end of a taper margin sig_channel=[4]; src_channel=[8]; FREQ=[0.05]; %frequency at which source and signal is low pass

filtered num_it=[60]; taper_sig=[0];

fname=['2deg_bin_0.5_SdS_alu_with table_numit_']; for taper_i=1:length(taper_sig) for it=1:length(num_it) for ch_i=1:length(src_channel) for roll_i=1:length(rolloff) for freq_i=1:length(FREQ) n=0; for mlat=lats n=n+1; m=0; for mlon=lons m=m+1; if mlon>180 mlon=-360+mlon; %The if statement

converts longitudes from a -180 to 180 scale to a 0 to 360 scale end [SdSFZ_common(:,n,m), SdSFZ_stack(:,n,m),

traces(:,n,m)] =

SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO,mlat, mlon,

2, 8, 4,

'H','D',rolloff(roll_i),num_it(it),0.2,4,4701,FREQ(freq_i),src_w1,src

_w2,sig_w1,sig_w2,taper_sig(taper_i)); %The above line finds the PdP functions

for data end end fname_out=[fname num2str(num_it(it)) '_t_sig_'

num2str(taper_sig(taper_i)) '_sig_ch_' num2str(sig_channel(ch_i))

'_src_ch_' num2str(src_channel(ch_i)) '_rolloff_'


save(fname_out,'lats','lons','SdSFZ_common','SdSFZ_stack','traces')


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end fname_out=[fname num2str(num_it(it)) '_t_sig_'




save(fname_out,'lats','lons','SdSFZ_common','SdSFZ_stack','traces') end fname_out=[fname num2str(num_it(it)) '_t_sig_'












save(fname_out,'lats','lons','SdSFZ_common','SdSFZ_stack','traces') end

function [ synth_now_stack_z,SdS_now_stack_z,traces ] =

SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO, mlat, mlon,

search_rad, source_ch, sig_ch, inD, process_stage, rolloff, num_it

,LPF,Sig2Noise,len_data,FREQ,src_w1,src_w2,sig_w1,sig_w2,taper_sig) %SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2 takes a midpoint

and a stack radius and performs deconvolution using cross correlation

on all recordings in the stack radius. %The input arguments of

SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2 are: %SNINFO=reference data %mlat=latitude of stacking midpoint %mlon=longitude of stacking midpoint %search_rad=radius about the midpoint where event and station pairs

are %stacked %source_ch=channel of the source %sig_ch=channel of the signal %inD=root directory of data %process_stage=F for filtered, S for signal/noise, B for beamed, D

for %debugged and beamed %rolloff=rolloff for taper subroutine %num_it=number of iterations for deconvolution %LPF=low pass filter value


132

%Sig2Noise=signal/noise ratio that needs to be satisfied for

processing

%The output arguments of

SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2 are: %synth_now_stack_z=combined stack produced from deconvolution %SdS_now_stack_z=individual stack produced from deconvolution %SdSF_phase_info=stores phase information from the deconvolution

% [ synth_now_stack_z,SdS_now_stack_z,SdSF_phase_info ] =

SdS_CC_2020real_SRC_from_TaperSIG_WID_depth_r_p2(SNINFO,10,-

152,0.25,8,2,'F','D',10,5,0.2,0,1,4701);

[ARCLEN, ~] = distance(mlat, mlon, SNINFO.Mlat, SNINFO.Mlon); in_rad_i = find(ARCLEN<=search_rad); len_in_rad=length(in_rad_i); traces=len_in_rad.*ones(1001,1); SdSF_phase_info.mlat=mlat; SdSF_phase_info.mlon=mlon; %Station_info = latitudes, longitudes and elevations of the %stations. %ARCLEN = distance in degrees between the given midpoint(mlat, mlon)

and all %the other stations in Station_info. %in_rad_i = holds the indices of all distances between

midpoint(mlat,mlon) %that are within(less than or equal) the search_rad.

m=0; signal_s=[];

for n=1:len_in_rad S_to_N=SNINFO.StoN_peak_low(in_rad_i(n)); %JSA_keep=SNINFO.jsa_keep(in_rad_i(n)); if S_to_N>=Sig2Noise %&& JSA_keep == 1 %Checks whether current

seismic file meets signal to noise and second QC parameters f1 = SNINFO.fileout(in_rad_i(n)).name; f1(1) = inD; f1(4) = process_stage %f11(n).name=f1 %save f11 f11 load(f1) %f1 stores the directory filenames in the search radius %f1 is edited to load the file the user wants based off the

inD and %process

[tstx,tsty]=size(data(sig_ch).data); %avoids seismic files

with empty recordings test_src=isempty(data(source_ch).data); if tstx == 0 || tsty == 0 || test_src == 1 else m=m+1; SdSF_phase_info.S(m).filename.name=f1; SdSF_phase_info.S(m).sta_lat=data(1).station_lat; SdSF_phase_info.S(m).sta_lon=data(1).station_lon; SdSF_phase_info.S(m).ev_lat=data(1).event_lat; SdSF_phase_info.S(m).ev_lon=data(1).event_lon; SdSF_phase_info.S(m).ev_depth=data(1).event_depth;


133

SdSF_phase_info.S(m).gcarc=data(1).gcarc;

signal_temp=data(sig_ch).data*(1/max(abs(data(source_ch).data)));

if length(signal_temp)<len_data signal_temp=[signal_temp;0]; %fixes seismic recording

with irregular lengths end shape=size(signal_temp); if shape(2) > shape(1) %after trace is normalized, makes

sure all trace data is of the same shape signal_temp=signal_temp'; end

dt=data(1).dt; T0 = data.SACT0; dtt(:,m)=double(data(10).dtt1); dzz(:,m)=double(data(10).zz1); %T0 = Time of first arrival %signal_temp stores the data from the signal channel %dt holds the sample rate of the data

signal(:,m) = GFILT_data_2015(signal_temp,FREQ,[5

5],dt,'l'); source(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0, rolloff,0,src_w1); source(:,m) =

Taperfilt_JH_2020_endtaper(source(:,m),dt,T0,10*rolloff,1,src_w2); signal(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0, rolloff,0,sig_w1); if taper_sig==1 signal(:,m) =

Taperfilt_JH_2020_endtaper(signal(:,m),dt,T0,rolloff,1,sig_w2); end signal_s(:,m)=[signal(:,m);

zeros(round(30/data(1).dt),1)]; source_s(:,m)=[round(zeros(30/data(1).dt,1));

source(:,m)]; %source and signal are both tapered using the

source_taper_2020_1 %subroutine

end end end

[sigx,~]=size(signal_s);

if sigx > 0 && len_in_rad>0 [synth_now_stack_z,SdS_now_stack_z,SdSF_phase_info] =

IT_DECON_JH_CC_WID_r_p2_John_edit(signal_s, source_s,

num_it,dt,LPF,dzz,dtt,len_data,SdSF_phase_info); else synth_now_stack_z=zeros(1001,1); SdS_now_stack_z=zeros(1001,1); SdSF_phase_info.S='This bin is empty'; end


134

end

function [synth_now_stack_z,PdP_SdS_now_stack_z,phase_info] =

IT_DECON_JH_CC_WID_r_p2_John_edit(signal,source,num_it,dt,LPF,dzz,dtt

,len_data,phase_info) %IT_DECON_JH_CC_WID_r_p2 takes in signal and source fuction pairs

from a %CMP gather and performs wavefield iterative deconvolution on them to %produce a stacked SdS receiver function

%The input arguments are: %signal=array of signal functions %source=array of source functions %num_it=number of iterations for deconvolution %dt=sampling rate in seconds/sample %LPF=low pass frequency for filter %dzz=depths for signal functions %dtt=travel times for signal functions %phase_parameters= phase info from deconvolution

[nrow,ncol]=size(signal); T=(1/LPF)/dt; signal_now=signal; synth_now=zeros(nrow,ncol); synth_now_stack_z=zeros(1001,1); phase_info.num_iter= num_it;

for n1=1:ncol dtt_resampled(:,n1)=interp1(dzz(:,n1),dtt(:,n1),[0:1000]); source_ac(:,n1)=convFD(source(:,n1), flipud(source(:,n1))); %in

this loop the auto correlation for all source functions are made [amp_ac(n1), i_ac(n1)]=max(abs(source_ac(:,n1))); sac=source_ac(:,n1); sac=flipud(sac(300:(length(sac))));%source was padded with 300

zeros, which is removed before depth conversion. sac is a temporary

variable which holds the current source being processed source_ac_z(:,n1)=interp1([0:(length(sac)-1)]*dt, sac,

dtt_resampled(:,n1)); end

source_ac_stack_z=mean(source_ac_z,2); [amp_ac_stack_z, ~] = max(abs(source_ac_stack_z)); %the

autocorrelations are stacked and the max amplitude and corressponding

index found

for i=1:num_it for n2=1:ncol sig_cc(:,n2)=convFD(signal_now(:,n2), flipud(source(:,n2))); DCC=sig_cc(:,n2); DCC=flipud(DCC(1:(length(sig_cc(:,n2))-300))); %signal was

padded with 300 zeros, which is removed before depth conversion. DCC


135

is a temporary variable which holds the current signal being

processed sig_cc_z(:,n2)=interp1([0:(length(DCC)-1)]*dt, DCC,

dtt_resampled(:,n2)); %the cross correlation functions are made and

depth converted end

sig_cc_stack_z=mean(sig_cc_z,2); [~, i_cc_stack_z] = max(abs(sig_cc_stack_z)); %the depth

converted cross correlation functions are stacked and the maximum

peak found i_cc_stack_z=i_cc_stack_z-1; % figure(69) % plot(sig_cc_stack_z,'linewidth',2) % title('sig cc stack z') % grid on

if i_cc_stack_z<=0 i_cc_stack_z=1; end synth_now_stack_z(i_cc_stack_z)=synth_now_stack_z(i_cc_stack_z) +

sig_cc_stack_z(i_cc_stack_z)/amp_ac_stack_z; %a synthetic SdS

function is made by plotting the deconvolved peak % figure(70) % plot(synth_now_stack_z, 'linewidth',3) % title('plot3 synth now stack Z') % grid on % pause

phase_info.SdSFcommon_depth(i)= i_cc_stack_z; phase_info.SdSFcommon_amp_depth(i)=

synth_now_stack_z(i_cc_stack_z);

for n3=1:ncol tn=interp1([0:1000],dtt_resampled(:,n3),i_cc_stack_z); %the

depth from the stack deconvolution is converted back to time. and a

time search window is made itn=len_data-round(tn/dt); t1=floor(itn-T); t2=ceil(itn+T);

if t1<0 t1=1; end if t2>length(sig_cc(:,n3)) t2=length(sig_cc(:,n3)); end

sig_cc_temp=sig_cc(:,n3); if sig_cc_stack_z(i_cc_stack_z)>0 [amp_cc,i_cc] = max((sig_cc_temp(t1:t2))); else [amp_cc,i_cc] = min((sig_cc_temp(t1:t2))); %Using the

time search window from the stacked, the individual cross correlation

functions are deconvolved end i_cc=i_cc+t1-1; i_now=i_cc-i_ac(n3)-1; %%%%I made it -1 instead of +1

if i_now<0, i_now=i_now+length(sig_cc_temp); end


136

if i_now>length(sig_cc_temp), i_now=i_now-

length(sig_cc_temp); end

synth_now(i_now,n3)=synth_now(i_now,n3) + amp_cc/amp_ac(n3);

%SdS functions are made from the individual signal functons sig_synth=convFD(synth_now(:,n3), source(:,n3)); %synthetic

signal made signal_now(:,n3)=signal(:,n3)-sig_synth; %synthetic signal

removed from original signal

phase_info.S(n3).SdSFindividual_time(i)= i_now*dt; %all

relevant phase info is saved into phase_info phase_info.S(n3).SdSFcommon_time(i)= (itn*dt); phase_info.S(n3).SdSFindividual_amp(i)= synth_now(i_now,n3); end

end

for n4=1:ncol PdP_SdS_cc=synth_now(:,n4); PdP_SdS_cc=flipud(PdP_SdS_cc(1:(length(PdP_SdS_cc)-300))); PdP_SdS_cc_z(:,n4)=interp1([0:(length(PdP_SdS_cc)-1)]*dt,

PdP_SdS_cc, dtt_resampled(:,n4)); %individual SdS functions depth

converted end

PdP_SdS_now_stack_z=mean(PdP_SdS_cc_z,2);%individual SdS functions

stacked

end

function f0=convFD(f1,f2) %convFD takes signal and/or source

functions and performs a wrap around convolution F1=fft(f1); F2=fft(f2); f0=real(ifft(F1.*F2)); end

function

[out_data]=Taperfilt_JH_2020_endtaper(in_data,dt,T0,rolloff,src_sig,m

od_T0)

taper = zeros(size(in_data)); %taper is a taper that is used to make the source. It is filled with

0 %except for the time window where the PdP or SdS is expected to

arrive

w1 = floor((T0+mod_T0)/dt); %w1 and w2 are the start and end of the time window where the PdP and %the SdS are expected to arrive


137

taper((w1):length(in_data)) = 1; taper_zone=[0:(1/rolloff):1]; taper(((w1)-rolloff):(w1))=taper_zone;

if src_sig==0 out_data = in_data.*taper; else taper=1-taper; out_data = in_data.*taper; end

end

copyright 2020, john sena akoto

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