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Published February 2015 | Published
Journal Article Open

Multi-events earthquake early warning algorithm using a Bayesian approach


Current earthquake early warning (EEW) systems lack the ability to appropriately handle multiple concurrent earthquakes, which led to many false alarms during the 2011 Tohoku earthquake sequence in Japan. This paper uses a Bayesian probabilistic approach to handle multiple concurrent events for EEW. We implement the theory using a two-step algorithm. First, an efficient approximate Bayesian model class selection scheme is used to estimate the number of concurrent events. Then, the Rao-Blackwellized Importance Sampling method with a sequential proposal probability density function is used to estimate the earthquake parameters, that is hypocentre location, origin time, magnitude and local seismic intensity. A real data example based on 2 months data (2011 March 9–April 30) around the time of the 2011 M9 Tohoku earthquake is studied to verify the proposed algorithm. Our algorithm results in over 90 per cent reduction in the number of incorrect warnings compared to the existing EEW system operating in Japan.

Additional Information

© 2014 The Authors. Published by Oxford University Press on behalf of The Royal Astronomical Society Accepted 2014 November 6. Received 2014 October 30; in original form 2014 July 6. We would like to thank the Japan Meteorological Agency and NIED for providing the seismic waveform data during the period of 2011 M9 Tohoku earthquake. This research was supported by the funding program for Next Generation World-Leading Researchers in Japan.

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Published - Geophys._J._Int.-2015-Wu-789-806.pdf


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