No abstract

In countries with low‐to‐moderate seismicity, the selection of appropriate ground‐motion prediction equations (GMPEs) to be used in a probabilistic seismic hazard analysis (PSHA) is a challenging step. Empirical observations of ground motion are limited, and GMPEs, when available, are generally based on stochastic simulations or adjusted empirical GMPEs from elsewhere. This article investigates the suitability of recent GMPEs to the United Kingdom. To this end, the spectral accelerations obtained from available instrumental ground‐motion data in the United Kingdom with magnitude lower than 4.5 are compared with the GMPEs’ predictions through the analysis of residuals and the application of statistical tests. To compensate for the scarcity of data for the magnitude range of interest in the PSHA, a macroseismic dataset is also considered. Macroseismic intensities are converted to peak ground acceleration (PGA) and statistically compared with the PGA predicted by the GMPEs. The GMPEs are then compared in terms of median ground‐motion prediction through Sammon’s maps to evaluate their similarities. The analyses from both datasets led to six suitable GMPEs, of which three are from the Next Generation Attenuation‐West2 project, one is European, one is based mainly on a Japanese dataset, and one is a stochastic GMPE developed specifically for the United Kingdom.

No abstract

In probabilistic seismic hazard assessment, the development of the seismic source characterization, especially the geometry of the seismic source models (SSMs), is controversial because it often relies on expert judgment with different interpretations of the available data from seismology, tectonics, and geology. Based on the same input datasets, different teams of experts may derive different SSMs. In this context, the verification of the models through the comparison against a set of observations is a crucial step. We present a statistical tool to compare the SSMs with the observed seismicity and rank these SSMs based on their ability to replicate the past seismicity. We simulate many synthetic catalogues derived from candidate SSMs and compare them with the observed catalogue of mainshocks using the Metropolis-Hastings Algorithm to select those that fit the observed catalogue. The candidate SSMs are then expressed by a probability density function (pdf) using the set of synthetic catalogues accepted by the Metropolis-Hastings Algorithm and the Bayesian inference. To help practitioners in earthquake and civil engineering understand how this tool works in practice, the proposed approach is applied to a proposed new nuclear site in the United Kingdom, Wylfa Newydd.