Bayesian Earthquake Forecasting Using Gaussian Process Modeling: GP-ETAS Applications

Abstract Numerous seismicity models are known to simulate different observed characteristics of earthquake occurrence successfully. However, their ability of prospective forecasting future events is a priori not always known. The recently proposed semiparametric model, Gaussian process epidemic-type aftershock sequence (GP-ETAS) model, which combines the ETAS model with GP modeling of the background activity, has led to promising results when applied to synthetic seismicity. In this study, we focus on the ability of GP-ETAS for different forecasting experiments in two case studies: first, the Amatrice, Italy, sequence during 2016 and 2017, and second, long-term seismicity in Southern California. The results indicate that GP-ETAS performs well compared with selected benchmark models. The advantages become particularly visible in cases with sparse data, in which GP-ETAS shows in general a more robust behavior compared to other approaches.