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Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California

2007; Seismological Society of America; Volume: 78; Issue: 1 Linguagem: Inglês

10.1785/gssrl.78.1.57

1938-2057

John E. Ebel, D. W. Chambers, A. L. Kafka, Jenny A. Baglivo,

Earthquake Detection and Analysis

Research Article| January 01, 2007 Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California John E. Ebel; John E. Ebel 1Weston Observatory, Department of Geology and Geophysics, Boston College Search for other works by this author on: GSW Google Scholar Daniel W. Chambers; Daniel W. Chambers 2Department of Mathematics, Boston College Search for other works by this author on: GSW Google Scholar Alan L. Kafka; Alan L. Kafka 1Weston Observatory, Department of Geology and Geophysics, Boston College Search for other works by this author on: GSW Google Scholar Jenny A. Baglivo Jenny A. Baglivo 2Department of Mathematics, Boston College Search for other works by this author on: GSW Google Scholar Author and Article Information John E. Ebel 1Weston Observatory, Department of Geology and Geophysics, Boston College Daniel W. Chambers 2Department of Mathematics, Boston College Alan L. Kafka 1Weston Observatory, Department of Geology and Geophysics, Boston College Jenny A. Baglivo 2Department of Mathematics, Boston College Publisher: Seismological Society of America First Online: 09 Mar 2017 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © 2007 by the Seismological Society of America Seismological Research Letters (2007) 78 (1): 57–65. https://doi.org/10.1785/gssrl.78.1.57 Article history First Online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation John E. Ebel, Daniel W. Chambers, Alan L. Kafka, Jenny A. Baglivo; Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters 2007;; 78 (1): 57–65. doi: https://doi.org/10.1785/gssrl.78.1.57 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search The quest to find successful methods to forecast earthquakes has proven to be very challenging. Useful earthquake forecasts require detailed specification of a number of variables, namely the epicenter, depth, time, and magnitude of the coming earthquake. While forecasting the times of strong aftershocks within the rupture zone of a strong earthquake has been developed with some success (e.g., Reasenberg and Jones 1989, 1994), forecasting the times of future strong earthquakes, even when their locations are known to occur within broad geographic areas, has not been very successful. The apparent success of the M8 algorithm in... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.