A0583
Title: Spatial distribution of coefficients of variation and Bayesian forecasts for recurrence intervals of earthquakes
Authors: Shunichi Nomura - The Institute of Statistical Mathematics (Japan) [presenting]
Yosihiko Ogata - Institute of Statistical Mathematics (Japan)
Abstract: A Bayesian method is proposed for probability forecasting for recurrent earthquakes of inland active faults in Japan. Renewal processes with the Brownian Passage Time (BPT) are applied for over a half of active faults in Japan by the Headquarters for Earthquake Research Promotion (HERP) of Japan. Long-term forecast with the BPT distribution needs two parameters; the mean and coefficient of variation (COV) for recurrence intervals. The HERP applies a common COV parameter for all of these faults because most of them have only one or a few specified paleoseismic events, which is not enough to estimate reliable COV values for respective faults. However, errors in COV estimates can make critical bias in forecasts and so COVs should be carefully selected for individual faults. The COVs of recurrence intervals depend on stress perturbation from nearby seismicity and have spatial trends. Thus we introduce a spatial structure on its COV parameter by Bayesian modeling with a Gaussian process prior. The COVs on active faults are correlated and take similar values for closely located faults. It is found that the spatial trends in the estimated COV values coincide with the density of active faults in Japan. We also show Bayesian forecasts by the proposed model using MCMC methods. Our forecasts are different from HERP's forecast especially on the active faults where HERP's forecasts are very high or low.
