@article{ref1,
title="A Bayesian methodology to update the probabilistic seismic hazard assessment",
journal="Bulletin of earthquake engineering",
year="2018",
author="Secanell, Ramon and Martin, Christophe and Viallet, Emmanuel and Senfaute, Gloria",
volume="16",
number="6",
pages="2513-2527",
abstract="This paper presents a Bayesian methodology for updating the seismic hazard curves. The methodology is based on the comparison of predictive exceedance rates of a fixed acceleration level (given by the seismic hazard curves) and the observed exceedance rates in some selected sites. The application of the methodology needs, firstly, the definition of a prior probabilistic seismic hazard assessment based in a logic tree. Each main branch corresponds to a probabilistic model of calculus of seismic hazard. The method considers that, initially (or a priori), the weights of all branches of the logic tree are equivalent. Secondly, the method needs to compile the observations in the region. They are introduced in a database containing the recorded acceleration data (during the instrumental period). Nevertheless, the instrumental period in stable zones (as France) shows only very low acceleration levels recorded during a short observation period. Then, a method to enlarge the REX (number of observations) is presented taking into account the historical data and defining &quot;synthetic&quot; accelerations in the sites of observation. The synthetic REX allows to expand the period of observation and to increase the acceleration thresholds used in the Bayesian updating process. The application of the Bayesian approach leads to a new and more objective definition of the weights of each branch of the logic tree and, therefore, to new seismic hazard curves (mean and centiles). The Bayesian approach doesn't change the probabilistic models (seismic hazard curves). It only modifies the weights of each branch of the logic tree.<p /> <p>Language: en</p>",
language="en",
issn="1570-761X",
doi="10.1007/s10518-017-0137-3",
url="http://dx.doi.org/10.1007/s10518-017-0137-3"
}