Siu-Kui Au

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,.

Despite longstanding interest and varied applications, the vibration behavior of trees remains poorly understood, largely due to methodological limitations. To more effectively identify and partition sources of natural variation in the modal properties of trees, there is a need for more reliable estimates derived from long-term observations. Using improved Bayesian methods, the modal properties of one green ash tree were identified over an entire growing season with simultaneous measurements of nearby weather conditions and phenological observations. The analysis confirmed many features of tree vibration observed in an earlier related study, including two prevalent close, nearly orthogonal modes and amplitude dependence for modal properties, but the more extensive and detailed measurements also revealed new characteristics, including changes in the arrangement of partial mode shapes and sizeable variation in modal properties over multiple time scales. For the leafless tree, the two modes were consistently oriented in directions aligned with the planting layout of surrounding trees, but their orientation varied erratically over time, while remaining roughly perpendicular to one another, during periods with leaves. Though some of the modal property variation closely paralleled other measurements, including weather conditions and vegetative phenophases, in physically reasonable ways, the reasons for other changes were not clear. Using a detailed assessment of the governing factors for estimate uncertainty, the cause of low-quality estimates was attributed to very close or nearly identical modes, and the causes and implications of close modes for tree vibration monitoring should be a main priority for future studies.