remote sensing using noise
DESCRIPTION
Remote sensing using noise. Peter Gerstoft, Scripps Institution of Oceanography. Paradigm shift: we are turning noise into useful data, from which structure information can be extracted. Noise gives similar information as using a source. Environmentally friendly! - PowerPoint PPT PresentationTRANSCRIPT
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Remote sensing using noisePeter Gerstoft, Scripps Institution of Oceanography
Paradigm shift: we are turning noise into useful data, from which structure information can be extracted.
Noise gives similar information as using a source. Environmentally friendly!
Noise Interferometry (NI) has seen remarkable growth in the last 5 years
Origin of seismic/acoustic noise
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Super Typhoon Ioke (in 2006)
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Tracking Tropical Cyclones
• Evidencing nonlinear wave-wave interactions in the deep ocean (Longuet-Higgins, 1950)
• Tracking wave-wave interactions rather than a storm itself
Zhang, Gerstoft, and Bromirski (under review)
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Track of storm from microseisms (0.2 Hz)
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Mechanism involves ocean acoustics!
Ocean waves
Deep ocean bottom
Classic seismic P-wave propagation
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1 2
*
Sources yielding constant time-delay τ lay on same hyperbola
τ=0
τ=L/c
-L/c +L/cτ
0
2→1 1→ 2
*
Free space noise correlation
C12(τ)
C12 ( ) P(r1, t)Pr t d t.
dC12 ( )
d G(t) G( t)
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Ambient noise EGFs (20-100 Hz)
Dis
tan
ce (
m)
Time (s)
EGF envelopes (dB) with modeled travel times (dotted) between hydrophones
Amplitude (dB) Wd=80 m
230 m long array Brooks and Gerstoft (JASA 2009a 2009b); Fried at al (JASAEL 2008)
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Green’s functions estimate
Wd=70 m
230 m long array
(a) Vertical lowered source
(b) Towed source
(c) Ship noise
(d) Ambient noise
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• HLA elements parameterized by distance and azimuth: model vector :
• Travel times from peak of empirical Green’s function: observed data vector:
• A priori array is straight
1
2 3 20 21origin
Noise array localization• Methodology adapted from Sabra [2005]
• Objective function minimize difference between observed traveltimes and computed traveltimes from model vector, whilst ensuring “smooth” fit
• Objective function minimized using MATLAB’s nonlinear least-squares function
• Six largest travel-time difference rejected for each computation
• Lower and upper limits set to half and twice a priori distances
• Variation of the smoothness ‘weighting’ seen to have negligible effect
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A priori vs a posteriori geometry
A priori geometry A posteriori geometry
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Siderius et al., JASA 2006,Gerstoft et al., JASA 2008, Harrison, JASA 2008Harrison, JASA 2009, Traer et al., JASA 2009,Siderius et al., JASA 2010
B1
B2
Using ambient noise on a drifting array we can map the bottom properties
Passive fathometer
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Fathometer comparison to seismic
South of Sicily (NURC: 32 phones spaced at 0.5 m)
Dabob Bay, Wa(16 phones spaced at 0.5 m)
Gerstoft et al., JASA 2008,
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Background
•Passive fathometerSiderius. JASA, 2010
Active source (Uniboom)
MVDR Passive fathometer
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Retrieving temporal velocity variations
A temporal change in velocity along the path between two stations is revealed as an increase in dt with propagation distance, when comparing the cross-correlations from two different time periods.
dt
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Measured velocity change associated with damage from earthquakes and volcanic precursors.
Brenguier et al, Science 2008
Velocity change across a fault
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Conclusion
• Noise provides useful signal• We can obtain a partial Greens function
Applications:• Locating noise sources• Used for obtaining Earth structure (many applications)• Fathometer• Structural health monitoring• Human body monitoring
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Downward beam (MAPEX2000bis)
fdesign c
2d, where d 0.5m
Above design frequency, downgoing noise appears as upgoing
Up
Down
Ang
le
Frequency (kHz)
32 element NURC array
SW06: d=4m => No fathometry!We need dense arrays to get sufficient resolution.
New Siderius arrays (d~0.5m) makes fathometry feasible.
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ErnestoSeismic Beamforming: a seismic array in California detected low frequency signals on Sep 2 from a direction consistent with the SW06 site.
=> Stay ashore
Ernesto provided ideal conditions for noise cross-correlation
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Future fathometer work
Experimental data shows array is subject to wave driven motion, preventing coherent averaging
• Model for amplitudes• Coherent averaging• Averaging time• Is the array moving up and down?
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Humphreys & Clayton
(JGR, 1990) Polet (G3, 2007)?
Storms
Teleseismic body-wave tomography (regional)
P Waves Imaging Earth Structure
Storms (seismic sources in open ocean) can fill azimuth gaps
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History of seismic/acoustic interferometry
• 1968 Claerbout• 1980’s experiment at Stanford• 1990’s helioseismology• 2001 Weaver and Lobkis• 2004 first papers in seismology, & ocean acoustics (Roux
and Kuperman)• 2008 book “Seismic interferometry: History and present
status”• 2009 book “Seismic interferometry”• 2009 ~100 papers/year; 3 in Science or Nature /year
Progress due to better computer resources, instrumentation and theory.
Still lots of low hanging fruits!
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Ocean noise interferometry publications www.mpl.ucsd.edu/people/pgerstoft
• Traer, Gerstoft and Hodgkiss (2010), Ocean bottom profiling with ambient noise: a model for the passive fathometer, submitted JASA.
• Siderius, Song, Gerstoft, Hodgkiss, Hursky, Harrison (2010), Adaptive passive fathometer processing, JASA.
• Brooks, Gerstoft (2009), Green’s function approximation from cross-correlation of active sources in the ocean, JASA.
• Brooks, Gerstoft (2009), Green's function approximation from cross-correlations of 20–100 Hz noise during a tropical storm, JASA.
• Traer, Gerstoft, Song. Hodgkiss (2009), On the sign of the adaptive passive fathometer impulse response, JASA.
• Gerstoft, Hodgkiss, Siderius, Huang, Harrison (2008), Passive fathometer processing, JASA.• Brooks, Gerstoft, Knobles (2008), Multichannel array diagnosis using noise cross-correlation,
JASA EL.• Traer, Gerstoft, Bromirski, Hodgkiss, Brooks (2008), Shallow-water seismo-acoustic noise
generation by Tropical Storms Ernesto and Florence, JASA EL.• Brooks and Gerstoft (2007), Ocean acoustic interferometry, JASA,
• Tomorrow:
Bill Hodgkiss nearfield geoacoustic inversion
Caglar Yardim PF and objective functions