Fast Probabilistic Seismic Hazard Analysis through Adaptive Importance Sampling
Bibliographic record
Abstract
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, where achieving a COV of 1% for a 10−4 annual hazard probability may require 108 MC samples. 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 Pacific Earthquake Engineering Research Center (PEER) PSHA benchmarks that cover various seismic sources, including areal, vertical, and dipping faults, as well as combined types. Our approach computes seismic hazard up to 3.7×104 and 7.1×103 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 approach is broadly applicable, especially for PSHA cases requiring extensive logic trees and epistemic uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".