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The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

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The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

Content Provider	Springer Nature Link
Author	REDDY, RAMAKRUSHNA NAIR, RAJESH R
Copyright Year	2013
Abstract	This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.
Starting Page	1423
Ending Page	1434
Page Count	12
File Format	PDF
ISSN	02534126
Journal	Journal of Earth System Science
Volume Number	122
Issue Number	5
e-ISSN	0973774X
Language	English
Publisher	Springer India
Publisher Date	2013-10-06
Publisher Institution	Indian Academy of Sciences
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wavelets earthquake magnitude determination P-wave amplitude time–frequency analysis support vector machines hyperplane Earth Sciences Extraterrestrial Physics, Space Sciences
Content Type	Text
Resource Type	Article
Subject	1900/1901

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