Fast Probabilistic Seismic Hazard Analysis Through Adaptive Importance Sampling
Description:
Probabilistic Seismic Hazard Analysis (PSHA) traditionally relies on two computationally intensive approaches: (a) Riemann Sum and (b) conventional Monte Carlo (MC) integration. The former requires fine slices across magnitude, distance, and ground motion, and the latter demands extensive synthetic earthquake catalogs. Both approaches become notably resource-intensive for low-probability seismic hazards. We introduce Adaptive Importance Sampling (AIS) PSHA, a novel framework to approximate optimal importance sampling (IS) distributions and dramatically reduce the number of MC samples to estimate hazards. We evaluate the efficiency and accuracy of our proposed framework using various seismic sources, including areal, vertical, and dipping faults, as well as combined types. Our approach computes seismic hazard up to 3.7x104 and 7.1x103 times faster than Riemann Sum and traditional MC methods, respectively, maintaining COVs below 1%. We also propose an enhanced approach with a "smart" AIS PSHA variant that leverages the sampling densities from similar ground motion intensities. This variant outperforms even "smart" implementations of Riemann Sum with enhanced grid discretizations by a factor of up to 130. Moreover, we demonstrate theoretically that optimal IS distributions are equivalent to hazard disaggregation distributions. Empirically, we show the approximated optimal IS and the disaggregation distributions are closely alike, e.g., with a Kolmogorov Smirnov statistic between 0.017 and 0.113. This novel approach also demonstrates extensibility to PSHA with multiple scenarios, i.e., accounting for epistemic uncertainty. We illustrate how to estimate the mean and fractile curves under this framework and highlight its potential to incorporate the distribution of epistemic uncertainty random variables in PSHA.
Session: Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources [Poster]
Type: Poster
Date: 4/16/2025
Presentation Time: 08:00 AM (local time)
Presenting Author: Soung Eil
Student Presenter: Yes
Invited Presentation:
Poster Number: 75
Authors
Soung Eil Houng Presenting Author Corresponding Author shoung@berkeley.edu University of California, Berkeley |
Luis Ceferino ceferino@berkeley.edu University of California, Berkeley |
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Fast Probabilistic Seismic Hazard Analysis Through Adaptive Importance Sampling
Session
Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources