Bayesian modeling of the temporal evolution of seismicity using the ETAS.inlabru package

Frontiers in Applied Mathematics and Statistics

Applied Mathematics and Statistics

Mark Naylor¹  Ian G. Main¹  Francesco Serafini²  Finn Lindgren³ 

[1] School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom;School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom;School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom;School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom;

关键词: Hawkes process;    INLA;    seismicity;    ETAS;    earthquake forecasting;   

DOI  :  10.3389/fams.2023.1126759

 received in 2022-12-18, accepted in 2023-01-23,  发布年份 2023

来源: Frontiers

【 摘 要 】

The epidemic type aftershock sequence (ETAS) model is widely used to model seismic sequences and underpins operational earthquake forecasting (OEF). However, it remains challenging to assess the reliability of inverted ETAS parameters for numerous reasons. For example, the most common algorithms just return point estimates with little quantification of uncertainty. At the same time, Bayesian Markov chain Monte Carlo implementations remain slow to run and do not scale well, and few have been extended to include spatial structure. This makes it difficult to explore the effects of stochastic uncertainty. Here, we present a new approach to ETAS modeling using an alternative Bayesian method, the integrated nested Laplace approximation (INLA). We have implemented this model in a new R-Package called ETAS.inlabru, which is built on the R packages R-INLA and inlabru. Our study has included extending these packages, which provided tools for modeling log-Gaussian Cox processes, to include the self-exciting Hawkes process that ETAS is a special case of. While we just present the temporal component here, the model scales to a spatio-temporal model and may include a variety of spatial covariates. This is a fast method that returns joint posteriors on the ETAS background and triggering parameters. Using a series of synthetic case studies, we explore the robustness of ETAS inversions using this method of inversion. We also included runnable notebooks to reproduce the figures in this article as part of the package's GitHub repository. We demonstrate that reliable estimates of the model parameters require that the catalog data contain periods of relative quiescence, as well as triggered sequences. We explore the robustness of the method under stochastic uncertainty in the training data and show that the method is robust to a wide range of starting conditions. We show how the inclusion of historic earthquakes prior to the modeled time window affects the quality of the inversion. Finally, we show that rate-dependent incompleteness of earthquake catalogs after large earthquakes have a significant and detrimental effect on the ETAS posteriors. We believe that the speed of the inlabru inversion, which includes a rigorous estimation of uncertainty, will enable a deeper exploration of how to use ETAS robustly for seismicity modeling and operational earthquake forecasting.

【 授权许可】

Unknown   
Copyright © 2023 Naylor, Serafini, Lindgren and Main.