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August 19, 2025Open Access

Fast Propagation of Epistemic Uncertainty in Seismic Hazard via Adaptive Importance Sampling

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Authors

SHSoung Eil HoungLCLuis CeferinoNANorman A. Abrahamson

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Overview

Gaussian Population Monte Carlo enhances hazard computation in seismic analysis, suggesting reduced bias and cost.

Key Points

  • The proposed G-PMC AIS framework accelerates computation of mean and fractile hazards while reducing bias.
  • Numerical experiments indicate accelerations ranging from 13 to 3,775 times compared to existing methods for seismic hazard analysis.
  • Significant reductions in computational costs are achieved while maintaining accuracy in results, supporting broader applications.
  • The framework mitigates biases inherent in the logic-tree approach, ensuring more reliable hazard estimates for seismic projects.

Cite This Study

Houng et al. (2025) studied this question.

synapsesocial.com/papers/68af4953ad7bf08b1ead4f3ehttps://doi.org/10.31224/5133
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Also Consider

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  1. 1Fast Probabilistic Seismic Hazard Analysis through Adaptive Importance Sampling2024
  2. 2Comparison of epistemic uncertainty captured in probabilistic seismic hazard assessments for critical infrastructure2026
  3. 3Advancing the Use of Simulated Ground Motions in Physics-Based Probabilistic Seismic Hazard Analysis from Research to Practice2026
  4. 4Logic-Tree Framework for Incorporating Kinematic Source Variability in Simulation-Based Seismic Hazard Estimation2026 · 3 citations
  5. 5Adaptive Radial-Based Importance Sampling for Efficient Estimation of Low Failure Probabilities in Geotechnical Random-Field Problems2026