California Earthquake Forecasts Based on Smoothed Seismicity: Model Choices

Qi Wang, David D. Jackson, & Yan Y. Kagan

Published 2010, SCEC Contribution #1450

We constructed five- and ten-year .smoothed seismicity. forecasts of moderate to large California earthquakes, and we examined the importance of several assumptions and choices. To do this we divided the available catalog into learning and testing periods and optimized parameters to best predict earthquakes in the testing period. Fourteen different five-year testing periods were considered, in which number of earthquakes varies from 18 to 63. We then compared the likelihood gain per target earthquake for the various choices. In this study we assumed that the spatial, temporal, and magnitude distributions are independent of one another, so that the joint probability distribution can be factored into those three components. We compared several disjoint test periods of the same length to determine the variability of the likelihood gain. The variability is large enough to mask the effects of some modeling choices. Stochastic declustering of the learning catalog produced a significantly better forecast, and representing larger earthquakes by their rupture surfaces provided a slightly better result, all other choices being equal. Inclusion of historical earthquakes and using an anisotropic smoothing kernel based on focal mechanisms failed to improve the forecast consistently. We chose a lower threshold magnitude for our learning catalog of 4.7, so that our results can be compared in the future to other forecasts relying on shorter catalogs with a smaller magnitude threshold.

Wang, Q., Jackson, D. D., & Kagan, Y. Y. (2010). California Earthquake Forecasts Based on Smoothed Seismicity: Model Choices. Bulletin of the Seismological Society of America, 101(3), 1422-1430. doi: 10.1785/0120100125.