Proximity to Past Earthquakes as a Least-Astonishing Hypothesis for Forecasting Locations of Future Earthquakes

The Cellular Seismology (CS) method of Kafka (2002, 2007) is presented as a leastastonishing null hypothesis that serves as a useful standard of comparison for other, more complex, spatial forecast methods (i.e., methods that forecast the locations, but not the times, of earthquakes). Spatial forecast methods based on analyses of earthquakes in California such as that of Ebel et al. (2007) and the Pattern Informatics method of Rundle et al. (2002, 2007) provide opportunities for comparing methods that incorporate information about rates of seismicity with a method (i.e., CS) that only assumes that future earthquakes will occur near epicenters of past earthquakes. The Ebel et al. (2007) 5-year forecast method (E07) maps the spatial distribution of rates of seismicity, and the Pattern Informatics method (PI) not only considers rates of seismicity but also incorporates temporal changes in local rates of seismicity as a measure of the potential for future earthquakes to occur at some location. Our comparison of success rates of the E07 method and the PI method with CS for earthquakes in California has yet to reveal any compelling evidence that inclusion of seismicity rates or temporal changes in local seismicity rates in a spatial forecast model improves the ability to forecast locations of earthquakes.