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

Strong 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 seismic clustering is the Epidemic Type Aftershock Sequence (ETAS) model, that 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 effects can substantially bias the parameter estimation and particularly lead to underestimated cluster sizes. In this article, we combine the approach of Grimm et al. (2021), which introduced a generalized anisotropic and locally restricted spatial kernel, with the ETAS-Incomplete (ETASI) time model of Hainzl (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 desaster response.

@article{id2817,
  author = {Grimm, Christian and Hainzl, Sebastian and K\"aser, Martin and K\"uchenhoff, Helmut},
  doi = {10.1007/s00477-022-02221-2},
  journal = {Stochastic Environmental Research and Risk Assessment},
  language = {en},
  title = {Solving three major biases of the ETAS model to improve forecasts of the 2019 Ridgecrest sequence},
  url = {https://doi.org/10.1007/s00477-022-02221-2},
  year = {2022},
}

%O Journal Article
%A Grimm, Christian
%A Hainzl, Sebastian
%A Käser, Martin
%A Küchenhoff, Helmut
%R 10.1007/s00477-022-02221-2
%J Stochastic Environmental Research and Risk Assessment
%G en
%T Solving three major biases of the ETAS model to improve forecasts of the 2019 Ridgecrest sequence
%U https://doi.org/10.1007/s00477-022-02221-2
%D 2022