Multiple-expert hazard/risk assessments have considerable precedent, particularly in the Yucca Mountain site characterization studies. A certain amount of expert knowledge is needed to interpret the geological data used in a probabilistic data analysis. As may be the situation in science, experts disagree on crucial points. Consequently, lack of consensus in some studies is a sure outcome. In this paper, we present a Bayesian approach to statistical modeling in volcanic hazard assessment for the Yucca Mountain site. Specifically, we show that the expert opinion on the site disruption parameterp is incorporated into the prior distribution, π(p), based on geological information that is available. Moreover, π(p) can combine all available geological information motivated by conflicting but realistic arguments (e.g., simulation, cluster analysis, structural control, ..., etc.). The incorporated uncertainties about the probability of repository disruptionpeventually will be averaged out by taking the expectation over π(p). We use the following priors in the analysis: (1) priors selected for mathematical convenience: Beta (r,s) for (r,s) = (2, 2), (3, 3), (5, 5), (2, 1),(2, 8), (8, 2), and (1, 1);and (2) three priors motivated by expert knowledge. Sensitivity analysis is performed for each prior distribution. Our study concludes that estimated values of hazard based on the priors selected for mathematical simplicity are uniformly higher than those obtained based on the priors motivated by expert knowledge. And, the model using the prior, Beta (8, 2), yields the highest hazard (=2.97 × 10-2. The minimum hazard is produced by the “three-expert prior” (i.e., values ofpare equally likely forp = 10-3, 10-2,and 10-1. The estimate of the hazard is 1.39 × 10-3, which is only about one order of magnitude smaller than the maximum value. The term, “hazard, ” is defined as the probability of at least one disruption of a repository at the Yucca Mountain site by basaltic volcanism for the next 10,000 years.
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