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Published June 2014 | Published
Journal Article Open

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


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‚Äźnegligible 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.

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).

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