A Nonparametric Hawkes Model for Forecasting California Seismicity

Joshua Seth Gordon, Frederic Paik Schoenberg, & Eric Warren Fox

Published May 18, 2021, SCEC Contribution #10932

A variety of nonparametric models have been proposed for estimating earthquake triggering. Building on Fox et al. (2016), we investigate the ability of the Model Independent Stochastic Declustering (MISD) method of Marsan and Lengline (2008) to estimate a spatial triggering function which can vary with direction, magnitude, and region. We develop an approach for local fault estimation and demonstrate forecasting methods which use the nonparametric estimates. Simulation studies are conducted to verify the effectiveness of the method and the nonparametric estimates are applied to a California earthquake catalog. Model forecast performance is evaluated retrospectively by the comparison of our models to the long term forecast of Helmstetter et al. (2007), using both deviance and Voronoi residuals. We show improved performance compared to Helmstetter et al. (2007) in various regions while using a full nonparametric estimation and forecasting approach.

Key Words
Point process, nonparametric estimation, Hawkes process, ETAS, Epidemic Type Aftershock Sequences, MISD, Model Independent Stochastic Declustering, Anisotropic ETAS

Citation
Gordon, J., Schoenberg, F., & Fox, E. (2021). A Nonparametric Hawkes Model for Forecasting California Seismicity. Bulletin of the Seismological Society of America, 111(4), 2216-2234. doi: 10.1785/0120200349.