Selecting Ground-Motion Models for Site-Specific PSHA: Adaptability versus Applicability

Abstract

ABSTRACT Capturing the center, the body, and the range of ground-motion predictions is an indispensable element of site-specific probabilistic seismic hazard analyses (PSHAs), for which the logic tree is the ubiquitous tool in current practice. The criteria for selecting the ground-motion models (GMMs) used in such studies have generally been focused on their potential applicability to the region and site for which the PSHA is being conducted. However, except for applications within the few regions with abundant ground-motion databases, it will rarely be the case that GMMs can be identified, which are perfectly calibrated to the characteristics of the target study region in terms of source and path properties. A good match between the generic site amplification model within the GMM and the site-specific dynamic response characteristics is equally, if not more, unlikely. Consequently, adjustments are likely to be made to the selected GMMs to render them more applicable to the target region and site. Empirical adjustments for host-to-target-region source differences using local recordings are unlikely to be robust, unless these have been generated by earthquakes from a wide range of magnitudes. Empirical adjustments for site characteristics are impossible, unless there are recordings from the target site. Therefore, the preferred approach makes parametric adjustments to empirical GMMs, isolating each host-to-target difference to map the individual contributions to the epistemic uncertainty. For such an approach to be applied, the emphasis moves from selecting GMMs on the basis of their applicability to focusing on their amenability to being adjusted to the target region and site. An adaptable equation is characterized by well-constrained host-region source, path, and site characteristics and a functional form in which response spectral accelerations scale with source, path, and site characteristics in a manner similar to the scaling implicit in stochastic simulations based on Fourier amplitude spectra.