Bulletin of the Seismological Society of America
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Bulletin of the Seismological Society of America; October 1997; v. 87; no. 5; p. 1140-1149
© 1997 Seismological Society of America
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wang, J.
Right arrow Articles by Teng, T.-l.
Right arrow Search for Related Content
GeoRef
Right arrow GeoRef Citation

Identification and picking of S phase using an artificial neural network

Jin Wang1 and Ta-liang Teng

Department of Earth Sciences University of Southern California, Los Angeles, California 90089-0740

Abstract

An artificial neural network (ANN) algorithm has been applied to the automatic picking of local and regional S phase. For a set of local three-component seismic data, a variety of features for signal detection and phase identification were analyzed in terms of sensitivity and efficiency. Comparing the performance of each feature in discriminating the local S phases, four features were selected as input attributes of the ANNS-phase picker: (1) the ratio between short-term average and long-term average, (2) the ratio between horizontal power and total power, (3) autoregressive model coefficients, and (4) the short-axis incidence angle of polarization ellipsoid. The four attributes were calculated in the frequency band of 2 to 8 Hz with a 2.56-sec moving window. This choice of frequency band and window length is appropriate for local microearthquake monitoring. The results of preliminary training and testing with a set of local earthquake recordings show that the ANNS-phase picker can achieve a good performance in identification and onset-time estimation for local S phases. In overall result, 86% correct rate of phase identification has been achieved by the trained ANNS-phase picker, 74% of them are precisely picked with less than 0.10-sec onset time error. We believe that the method presented here is a promising approach to automatic phase identification and onset-time estimation.

Footnotes

1 Present address: Center for Monitoring Research, 1300 N. 17th Street, Suite 1450, Arlington, Virginia 22209.






HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 1997 by the Seismological Society of America.