A hidden Markov model for earthquake declustering
The hidden Markov model (HMM) and related algorithms provide a powerful framework for statistical inference on partially observed stochastic processes. HMMs have been successfully implemented in many disciplines, though not as widely applied as they should be in earthquake modeling. In this article,...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Online Access: | https://ink.library.smu.edu.sg/soe_research_all/17 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1016&context=soe_research_all |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | The hidden Markov model (HMM) and related algorithms provide a powerful framework for statistical inference on partially observed stochastic processes. HMMs have been successfully implemented in many disciplines, though not as widely applied as they should be in earthquake modeling. In this article, a simple HMM earthquake occurrence model is proposed. Its performance in declustering is compared with the epidemic-type aftershock sequence model, using a data set of the central and western regions of Japan. The earthquake clusters and the single earthquakes separated using our model show some interesting geophysical differences. In particular, the log-linear Gutenberg-Richter frequency-magnitude law (G-R law) for the earthquake clusters is significantly different from that for the single earthquakes. |
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