Publication Type
Journal Article
Publication Date
3-2010
Abstract
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.
Discipline
Geographic Information Sciences | Nature and Society Relations
Research Areas
Economic Theory
Publication
Journal of Geophysical Research
Volume
115
Issue
B3
ISSN
0148-0227
Identifier
10.1029/2008JB005997
Publisher
American Geophysical Union (AGU) / Wiley
Citation
WU, Zhengxiao.
A hidden Markov model for earthquake declustering. (2010). Journal of Geophysical Research. 115, (B3),.
Available at: https://ink.library.smu.edu.sg/soe_research_all/17
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org./10.1029/2008JB005997