HR: 0800h
AN: NG41B-0521    [Abstracts]
TI: Bayesian Analysis of the Pattern Informatics Technique
AU: * Cho, N
EM: ncho3@uwo.ca
AF: Department of Earth Sciences, University of Western Ontario, 1151 Richmond St., London, ON N6A 5B7, Canada
AU: Tiampo, K
EM: ktiampo@uwo.ca
AF: Department of Earth Sciences, University of Western Ontario, 1151 Richmond St., London, ON N6A 5B7, Canada
AU: Klein, W
EM: klein@physics.bu.edu
AF: Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, United States
AU: Rundle, J
EM: rundle@cse.ucdavis.edu
AF: Center for Computational Science and Engineering, University of California, One Shields Avenue, Davis, CA 95616, United States
AB: The pattern informatics (PI) [Rundle et al., 2000; Tiampo et al., 2002; Holliday et al., 2005] is a technique that uses phase dynamics in order to quantify temporal variations in seismicity patterns. This technique has shown interesting results for forecasting earthquakes with magnitude greater than or equal to 5 in southern California from 2000 to 2010 [Rundle et al., 2002]. In this work, a Bayesian approach is used to obtain a modified updated version of the PI called Bayesian pattern informatics (BPI). This alternative method uses the PI result as a prior probability and models such as ETAS [Ogata, 1988, 2004; Helmstetter and Sornette, 2002] or BASS [Turcotte et al., 2007] in order to obtain the likelihood. Its result is similar to the one obtained by the PI: the determination of regions, known as hotspots, that are most susceptible to the occurrence of events with M=5 and larger during the forecast period. As an initial test, retrospective forecasts for the southern California region from 1990 to 2000 were made with both the BPI and the PI techniques, and the results are discussed in this work.
DE: 4430 Complex systems
SC: Nonlinear Geophysics [NG]
MN: 2007 Fall Meeting