TECHNIQUES AND PARAMETERS TO ANALYZE SEISMICITY PATTERNS ASSOCIATED WITH LARGE EARTHQUAKES

A pattern recognition algorithm is developed to provide potential impr ovements over existing earthquake prediction practices. The parameters employed in the analysis include degree of spatial nonrandomness in t wo distance ranges, spatial correlation dimension, spatial repetitiven ess of earthquakes with a similar size, average depth of events, time interval for the occurrence of a constant number of events, and ratio of the numbers of events in two magnitude intervals. The parameter tem poral variations are compared quantitatively with the time series of l arge events using a technique of association in time. The significance of the association frequencies is evaluated by comparison with chance associations estimated from corresponding simulated random time serie s. The developed techniques differ from existing approaches in the fol lowing aspects. The parameters here emphasize the spatial distribution of earthquakes. Possible correlations among the parameters are evalua ted to determine the final set of parameters to be monitored Threshold values for the assumed anomalies are chosen with consideration of pro perties of the available earthquake catalogs, such as the number of la rge events to be retrospectively predicted. Equal weight is given to b oth locally high and locally low parameter values. Care is taken to di stinguish between anomalies preceding large events and those following previous events. It is shown that the relationship between precursory local extrema and precursory trends is nonunique, with precursory loc al extrema of the same type frequently associated with opposite observ able precursory trends. The application of the seismicity parameters a nd pattern recognition techniques is demonstrated using synthetic eart hquake catalogs generated by models of segmented fault systems in a th ree-dimensional elastic solid [Ben-Zion, 1996].