automatic clustering of seismic events in an on-site ...€¦ · ’94 project which measured...
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Automatic clustering of seismic eventsin an On-Site Inspection scenario
Benjamin Sick ([email protected]), Manfred JoswigInstitute for Geophysics, University of Stuttgart
Science & Technology 2011
IntroductionDuring an On-site Inspection (OSI) it is essential to get a fast overview of recorded seismicsignal classes to evaluate the local seismicity, and in particular to investigate on suspiciousevents eventually representing aftershocks from an underground nuclear explosion (UNE).The seismic aftershock monitoring system (SAMS) of an OSI comprises up to fifty mini-arrayseach having six traces of continuous waveform data. The sought-after events can have amagnitude as low as ML -2.0, and a duration of just a few seconds which makes it particularlyhard to discover them in the large data set.
Event detectionTo overcome the first challenge of event detection we use a special form of spectrogram, thefour-traces Super-Sonograms to rise these signals from stationary background noise and totest on array-wide signal coherency. The Super-Sonograms proved to be indispensable inmanual screening of IFE08 data and got implemented into SAMS.
Mini-arrays (SNS)
Seismic measurement during an OSI is donewith mini-arrays (Seismic Navigating Systems,SNS).One SNS consists of:I 1 central 3-component seismometerI 3 satellite vertical seismometersThe inspection area for an OSI is limited to1000 m2 and each SNS covers an area of ap-proximately 30 m2 to detect events of magni-tude ML < 2.⇒ It requires approximately 35 SNS to
cover an inspection area
CenterEastWest
North
120°20 - 100m
Sonogram
Aftershocks of UNEs are often too weak and too short to be recognized in the seismogramsof the huge waveform datasets which have to be analyzed in nearly real-time. Sonograms[Joswig(1990)] are based on spectrograms and allow a fast visual interpretation of energiesover time and frequency. By applying multiple signal enhancing steps the identification of verysmall scale events is greatly improved and event patterns are prominent even when analysinglarge datasets with varying noise conditions:I Power spectral density
through short-term-fourier-transformation (STFT)
I Logarithmic frequency- andamplitude-scaling
I Frequency dependant noise-adaptation, muting andprewhitening
I Color scale optimized for vi-sual inspection
Time
Amplitude
Time
Frequency
Increasingenergy
⇒ For STFT(ω, t) > µ(ω) + σ(ω): Sonogramm(ω, t) = (int)log2
(STFT(ω,t)−µ(ω)
σ(ω)
)+ 1
Super-Sonogram
Single stations of one SNSare within 200 m of distancewhich makes it possible to com-bine the four vertical tracesof one SNS into a Super-Sonogram. Each Pixel ofthe Super-Sonogram consists offour sub-pixels, each from onevertical trace of the SNS. Array-wide signal coherency can bechecked fast and the data of allstations of one campaign can bedisplayed on one screen.
W N
C E
NWEC
SNS
1C
3C
1C
1C
Center
EastWest
North
Further information
http://www.nanoseismic.net
SoftwareTo be able to meet the hard time schedules of an OSI which re-quire to analyse the seismic data of all SNS of one day within thefollowing day, a software suite was developed which focuses onthe fast scanning for small scale events in huge datasets, calledSAMS - GeophysSuite. It is written in Java to be multi platformcompatible and consists of multiple modules which synchronizecurrent inspection times:
I SonoView, fast scanning of Super-Sonograms of numerousSNS
I TraceView, map overview of stations and visualization of seis-mograms of neighbouring SNS
I HypoLine, interactive localization and magnitude estimation
TraceView
HypoLine
Timesynchronization
Timesynchronization
SonoView
13 minutes
Overlap
25S
NS
Unsupervised classificationWe test unsupervised classifica-tion on the dataset of the PISCO’94 project which measured seis-micity in the central Andes ofnorthern Chile. We try to discrimi-nate five types of events which oc-cur in different regions of the mea-surement area.
−72˚ −70˚ −68˚ −66˚
−24˚
−22˚
50 km
N
−250
−200
−150
−100
−50
0
Hyp
oce
nte
r d
ep
th (
km
)
QUARRY
NORTH
MAINDEEP
CRUST
Station A04Cluster borders
SOUTH AMERICA
Feature extraction
The main feature extraction is done by the Sonogram computation which is enhancedby the following methods:I Normalization to provide an
amplitude-invariant clusteringI Transformation with Principal Com-
ponent Analysis (PCA) of the first 5principal components
I 2-D similarity function optimizedfor fuzzy comparison of Super-Sonograms
Types Seismograms FeaturesCrust
Main
North
Deep
Quarry
Visualization of single principal components
The dominant features of the SOM clustering andsonogram compilation of the arranged representa-tives can be analyzed with PCA. Here we show theadjustment of Sonograms along the 5 principal com-ponents with the largest variance. It can be seene.g. that the first principal component is mainly re-sponsible for energy amplitude variations and ex-tension of S-P-onset times.
PC1 PC2 PC3 PC4 PC5
Self-organizing Map [Kohonen(2001)]
Grouping event classes without prior knowledge, i.e., the task of unsupervised classification is handledby Self-organizing Maps (SOM). The SOM creates a map of representatives for each event type ar-ranged by proximity of features, giving us a synoptic and topological overview of the acquired seismicdata. Moreover first tests showed that the obtained SOM is well applicable for classification of newevents that are passed to it (94.2 % success rate with the given dataset).
0 2 4 6 8
0
2
4
Map width
Map
heigh
t CRUST
MAIN
NORTH
DEEP
QUARRY
DISCARDED
Outlook
The data of only one station of the dataset was used for the unsupervised classification. As a nextstep surrounding stations will be integrated in the classification process and decisions based onsignal coherency and logic will be involved.
Acknowledgements
The GeophysSuite software development is funded by the CTBT in line with the OSI SeismicAftershock Monitoring System development. The PISCO ’94 dataset was used for this study whichis free available through the GEOFON (http://geofon.gfz-potsdam.de/geofon/) project,Hypocenters were calculated by Graeber [Graeber(1997)].
References
I Graeber, F., 1997.Seismische Geschwindigkeiten und Hypozentren in den sudlichenzentralen Anden aus der simultanen Inversion von Laufzeitdatendes seismologischen Experiments PISCO’94 in Nordchile, Diss.,Freie Universitat Berlin.
I Joswig, M., 1990.Pattern recognition for earthquake detection, Bull. seism. Soc. Am., 80(1),170–186.
I Kohonen, T., 2001.Self-organizing maps, vol. 30 of Springer Ser. in Inf. Sci., Springer.
Institute for Geophysics, University of Stuttgart http://www.geophys.uni-stuttgart.de