Multi-events earthquake early warning algorithm using a Bayesian approach
Multi-events earthquake early warning algorithm using a Bayesian approach
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DOI：
10.1093/gji/ggu437
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发表时间：
2015-02
影响因子：
2.8
通讯作者：
Stephen Wu;M. Yamada;K. Tamaribuchi;J. Beck
中科院分区：
文献类型：
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作者：
Stephen Wu;M. Yamada;K. Tamaribuchi;J. Beck
目前的地震预警系统缺乏适当处理多个并发地震的能力，这导致了2011年日本东北地震序列中的许多误报。本文使用贝叶斯概率方法来处理多个并发事件的EEW。我们使用两步算法实现理论。首先，一个有效的近似贝叶斯模型类选择方案被用来估计并发事件的数量。然后，采用Rao-Blackwellized重要抽样方法，采用序贯建议概率密度函数，对地震参数（震源位置、发震时间、震级和局部烈度）进行估计。以2011年东北M9地震前后2个月（2011年3月9日至4月30日）的数据为例，对所提出的算法进行了验证。我们的算法的结果在90%以上的错误警告的数量减少相比，现有的EEW系统在日本运行。
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.