Bayesian inference of stress release models applied to some Italian seismogenic zones

Abstract

In this paper, we evaluate the seismic hazard of a region in southern Italy by analysing stress release models from the Bayesian viewpoint; the data are drawn from the most recent version of the parametric catalogue of Italian earthquakes. For estimation we just use the events up to 1992, then we forecast the date of the next event through a stochastic simulation method and we compare the result with the really occurred shocks in the span 1993–2002. The original version of the stress release model, proposed by Vere-Jones in 1978, transposes Reid's elastic rebound theory in the framework of stochastic point processes. Since the nineties enriched versions of this model have appeared in the literature, applied to historical catalogues from China, Iran, Japan; they envisage the identification of independent or interacting tectonic subunits constituting the region under exam. It follows that the stress release models, designed for regional analyses, are evolving towards studies on fault segments, realizing some degree of convergence to those models that start from an individual fault and, considering the interactions with nearby segments, are driven to studies on regional scale. The optimal performance of the models we consider depends on a set of choices among which: the seismogenic region and possible subzones, the threshold magnitude, the length of the time period. In this paper, we focus our attention on the influence of the subdivision of the region under exam into tectonic units; in the light of the recent studies on the fault segmentation model of Italy we propose a partition of Sannio-Matese-Ofanto-Irpinia, one of the most seismically active region in southern Italy. The results show that the performance of the stress release models improves in terms of both fitting and forecasting when the region is split up into parts including new information about potential seismogenic sources.