Earthquake hazards across the San Francisco Bay Area: a methodology for probabilistic seismic hazard analysis of strong shaking occurring anywhere in a region

SUMMARY

Probabilistic seismic hazard analysis is commonly used to estimate the likelihood of ground motion exceeding a certain threshold, over a given time horizon, at a single location. This is different than knowing the probability that any location within a region will experience ground motion exceeding that threshold, information that would be useful for assessing short-term hazards from aftershocks or induced seismicity, guiding emergency managers’ response planning, identifying which areas an earthquake early warning system should alert, or even validating existing probabilistic seismic hazard models. In this paper, we develop a methodology for estimating ground motion hazard across a region, specifically determining the probability of ground shaking anywhere in that region exceeding a prescribed threshold. We benchmark the efficiency of several stochastic sampling algorithms; compare various spatial covariance models including a new spatial covariance model based on dense observations of shaking from aftershocks of the 2019 Ridgecrest, California earthquake sequence; determine the minimum required spatial discretization for estimating shaking hazard across a region and explore assorted computational efficiencies. With this new methodology, we then present a case study of seismic hazard across California’s San Francisco Bay Area, computing probabilities that somewhere in the Bay Area will experience peak ground acceleration exceeding 0.1g, 0.2g, 0.4g or 0.8g at least once over timescales of one to 30 yr. Finally, we use these results to explore the impacts on the estimated seismic hazard of short-term versus long-term earthquake rates, ground motion variability and source selection,.