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Failure models driven by a self-correcting point process in earthquake occurrence modeling

Rotondi, R., Varini, E.
Stochastic environmental research and risk assessment 2019 v.33 no.3 pp. 709-724
Bayesian theory, Weibull statistics, algorithms, data collection, earthquakes, geophysics, models
The long-term recurrence of strong earthquakes is often modeled according to stationary Poisson processes for the sake of simplicity. However, renewal and self-correcting point processes (with nondecreasing hazard functions) are more appropriate. Short-term models mainly fit earthquake clusters due to the tendency of an earthquake to trigger other earthquakes. In this case, self-exciting point processes with nonincreasing hazard are especially suitable. To provide a unified framework for analysis of earthquake catalogs, Schoenberg and Bolt proposed the short-term exciting long-term correcting model in 2000, and in 2005, Varini used a state-space model to estimate the different phases of a seismic cycle. Both of these analyses are combinations of long-term and short-term models, and the results are not completely satisfactory, due to the different scales at which these models appear to operate. In this study, we propose alternative modeling. First, we split a seismic sequence into two groups: the leader events, non-secondary events the magnitudes of which exceed a fixed threshold; and the remaining events, which are considered as subordinate. The leader events are assumed to follow the well-known self-correcting point process known as the stress-release model. In the interval between two subsequent leader events, subordinate events are expected to cluster at the beginning (aftershocks) and at the end (foreshocks) of that interval; hence, they are modeled by a failure process that allows bathtub-shaped hazard functions. In particular, we examined generalized Weibull distributions, as a large family that contains distributions with different bathtub-shaped hazards, as well as the standard Weibull distribution. The model is fit to a dataset of Italian historical earthquakes, and the results of Bayesian inference based on the Metropolis–Hastings algorithm are shown.