Solving three major biases of the ETAS model to improve forecasts of the 2019 Ridgecrest sequence

Abstract

AbstractStrong earthquakes cause aftershock sequences that are clustered in time according to a power decay law, and in space along their extended rupture, shaping a typically elongate pattern of aftershock locations. A widely used approach to model earthquake clustering, the Epidemic Type Aftershock Sequence (ETAS) model, shows three major biases. First, the conventional ETAS approach assumes isotropic spatial triggering, which stands in conflict with observations and geophysical arguments for strong earthquakes. Second, the spatial kernel has unlimited extent, allowing smaller events to exert disproportionate trigger potential over an unrealistically large area. Third, the ETAS model assumes complete event records and neglects inevitable short-term aftershock incompleteness as a consequence of overlapping coda waves. These three aspects can substantially bias the parameter estimation and lead to underestimated cluster sizes. In this article, we combine the approach of Grimm et al. (Bulletin of the Seismological Society of America, 2021), who introduced a generalized anisotropic and locally restricted spatial kernel, with the ETAS-Incomplete (ETASI) time model of Hainzl (Bulletin of the Seismological Society of America, 2021), to define an ETASI space-time model with flexible spatial kernel that solves the abovementioned shortcomings. We apply different model versions to a triad of forecasting experiments of the 2019 Ridgecrest sequence, and evaluate the prediction quality with respect to cluster size, largest aftershock magnitude and spatial distribution. The new model provides the potential of more realistic simulations of on-going aftershock activity, e.g. allowing better predictions of the probability and location of a strong, damaging aftershock, which might be beneficial for short term risk assessment and disaster response.