Updating a probabilistic seismic hazard assessment with instrumental and historical observations based on a Bayesian inference

Since the basic work of Cornell, many studies have been conducted in order to evaluate the probabilistic seismic hazard (PSHA) at a given site or at a regional scale. In general, results of such studies are used as inputs for regulatory hazard maps or for risk assessments. Such approaches are nowadays considered as well established and come more and more used worldwide, in addition to deterministic approaches. Nevertheless, some discrepancies have been observed recently in some PSHA, especially from studies conducted in areas with low to moderate seismicity. The lessons learnt from these results lead to conclude that, due to epistemic uncertainties inherent to such a domain, some deterministic choices have to be made and, depending on expert judgments, may lead to strong differences in terms of seismic motion evaluation. In this context, the objective of this paper is to present a methodology that can be used to take into consideration instrumental and historical observations in order to reduce epistemic uncertainties in a PSHA. The method developed here is based on a Bayesian inference technique used in order to quantify the likelihood of the prior estimation, and finally update the PSHA. The period of observation under consideration is the completeness period for each set of observation used. The updating process is developed at a regional scale, over a significant number of stations. The potential correlation between points of observation is also discussed and accounted for. Finally, a case of application is proposed on the French metropolitan territory to demonstrate the efficiency of this updating method and draw perspectives for further applications.