HR: 17:45h
AN: NG54A-08    [Abstracts]
TI: Modification of the Pattern Informatics Method for Forecasting Large Earthquake Events Using Complex Eigenvectors
AU: * Holliday, J R
EM: holliday@cse.ucdavis.edu
AF: Center for Computational Science and Engineering, University of California, One Shields Avenue, Davis, CA 95616-8677 United States
AU: * Holliday, J R
EM: holliday@cse.ucdavis.edu
AF: Department of Physics, University of California, One Shields Avenue, Davis, CA 95616-8677 United States
AU: Rundle, J B
EM: jbrundle@ucdavis.edu
AF: Center for Computational Science and Engineering, University of California, One Shields Avenue, Davis, CA 95616-8677 United States
AU: Rundle, J B
EM: jbrundle@ucdavis.edu
AF: Department of Physics, University of California, One Shields Avenue, Davis, CA 95616-8677 United States
AU: Tiampo, K F
EM: ktiampo@uwo.ca
AF: Department of Earth Sciences, University of Western Ontario, Biology and Geological Sciences Bldg., London, ON N6A 5B7 Canada
AU: Klein, B
EM: klein@buphyc.bu.edu
AF: Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA 02215 United States
AU: Donnellan, A
EM: donnellan@jpl.nasa.gov
AF: Earth and Space Sciences Division, Jet Propulsion Laboratory, Mail Stop 183-335, 4800 Oak Grove Drive, Pasadena, CA 91109-8099 United States
AB: Recent studies in the literature have shown that real-valued principal component analysis can be applied to earthquake fault systems for forecasting and prediction. In addition, theoretical analysis indicates that earthquake stresses may obey a wave-like equation, having solutions with inverse frequencies for a given fault similar to those that characterize the time intervals between the largest events on the fault. It is therefore desirable to apply complex PCA analysis to develop earthquake forecast algorithms. In this analysis we modify the Pattern Informatics method of earthquake forecasting to take advantage of the wave-like properties of seismic stresses and utilize the Hilbert transform to create complex eigenvectors out of measured time series. We show that PI analysis using complex eigenvectors create short-term forecast hot-spot maps which are better correlated with actual future events and create a forecast map for large ($M>5$) earthquake events in Southern California over the time period 1 August 2004 through 31 July 2009.
DE: 7223 Seismic hazard assessment and prediction
DE: 7260 Theory and modeling
DE: 7294 Instruments and techniques
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting