Mixture models for improved short-term earthquake forecasting

David A. Rhoades, & Matthew C. Gerstenberger

Published 2009, SCEC Contribution #1186

The short-term earthquake probability (STEP) forecasting model applies the modified Omori law and the Gutenberg-Richter law to clusters of earthquakes. It is intended mainly to forecast aftershock activity, and depends on a time-invariant background model to forecast most of the major earthquakes. On the other hand, the long-range earthquake forecasting model EEPAS -- "Every Earthquake a Precursor According to Scale" exploits the precursory scale increase phenomenon and associated predictive scaling relations to forecast the major earthquakes months, years or decades in advance, depending on magnitude. Both models have been shown to be more informative than time-invariant models of seismicity. By forming a mixture of the two, we aim to create an even more informative forecasting model. Using the ANSS catalogue of California over the period 1984 - 2004, the optimal mixture model for forecasting earthquakes with magnitude M > 5.0 is a convex linear combination consisting of 0.42 of the EEPAS forecast and 0.58 of the STEP forecast. This mixture gives an average probability gain of more than 2 compared to each of the individual models. Several different mixture models will be submitted to the CSEP Testing Center at the Southern California Earthquake Center to ascertain whether this result is borne out by real-time tests of the models against future earthquakes.<br/><br/>

Rhoades, D. A., & Gerstenberger, M. C. (2009). Mixture models for improved short-term earthquake forecasting. Bulletin of the Seismological Society of America, 99(2a), 636-646. doi: 10.1785/0120080063.