Abstract
Ground-motion models (GMMs) are a key driver for the results of probabilistic seismic hazard analyses and their uncertainty. GMMs that bridge seismological and empirical approaches are an effective tool to represent the distribution of ground motion and its uncertainty in seismic hazard assessment. A methodology is presented that uses ground-motion data recorded at seismograph sites in eastern North America and shows how they can be used to calibrate simple scalable seismological models of ground-motion generation and propagation. Such GMMs can directly account for the gross features of source scaling (magnitude and stress parameter), attenuation, site response, and kappa effects. It is shown that, by application of appropriate GMM strategies, sigma (aleatory uncertainty) could be greatly reduced, resulting in lower calculated hazard for nuclear plants founded on rock. This reduction in sigma requires that high-quality seismic monitoring (e.g., broadband seismograph stations) be installed and operated over a period of years (in addition to strong-motion stations), and that an ongoing investment be made in data analysis and targeted GMM development using the data.