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Bayesian Approach for Identification of Multiple Events in an Early Warning System

Liu, Annie and Yamada, Masumi (2014) Bayesian Approach for Identification of Multiple Events in an Early Warning System. Bulletin of the Seismological Society of America, 104 (3). pp. 1111-1121. ISSN 0037-1106. https://resolver.caltech.edu/CaltechAUTHORS:20140731-082120109

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Abstract

The 2011 Tohoku earthquake (M_w 9.0) was followed by a large number of aftershocks that resulted in 70 early warning messages in the first month after the mainshock. Of these warnings, a non‐negligible fraction (63%) were false warnings in which the largest expected seismic intensities were overestimated by at least two intensities or larger. These errors can be largely attributed to multiple concurrent aftershocks from distant origins that occur within a short period of time. Based on a Bayesian formulation that considers the possibility of having more than one event present at any given time, we propose a novel likelihood function suitable for classifying multiple concurrent earthquakes, which uses amplitude information. We use a sequential Monte Carlo heuristic whose complexity grows linearly with the number of events. We further provide a particle filter implementation and empirically verify its performance with the aftershock records after the Tohoku earthquake. The initial case studies suggest promising performance of this method in classifying multiple seismic events that occur closely in time.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1785/0120130208 DOIArticle
http://www.bssaonline.org/content/104/3/1111PublisherArticle
http://bssa.geoscienceworld.org/content/104/3/1111PublisherArticle
Additional Information:© 2014 Seismological Society of America. The first author would like to thank Jim Mori at Kyoto University for the generous hosting and the warm welcome she received from all the group members. The authors would also like to thank Japan Meteorological Agency (JMA) for providing the strong-motion seismic data. This work was generously funded by the 2011 National Science Foundation East Asian and Pacific Summer Institute (EAPSI) Fellowship and the Funding Program for Next Generation World-Leading Researchers (NEXT Program).
Funders:
Funding AgencyGrant Number
2011 National Science Foundation East Asian and Pacific Summer Institute (EAPSI) FellowshipUNSPECIFIED
Funding Program for Next Generation World-Leading Researchers (NEXT Program)UNSPECIFIED
Issue or Number:3
Record Number:CaltechAUTHORS:20140731-082120109
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140731-082120109
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47687
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:31 Jul 2014 15:47
Last Modified:03 Oct 2019 06:55

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