Smoothing-Based Aftershock Probabilistic Seismic Hazard Assessment Using the Spatiotemporal ETAS Model
Bibliographic record
Abstract
ABSTRACT Probabilistic seismic hazard analysis (PSHA) is generally based on computing time-invariant occurrence rates of mainshocks using the Poisson process. However, aftershock probabilistic seismic hazard analysis (APSHA) allows for assessing time-varying aftershock occurrence rates within a short-term seismic hazard mitigation framework. Our proposed methodology of APSHA develops a smoothing-based analytical formulation to capture the spatial distribution and temporal evolution of aftershock sequences using the spatiotemporal epidemic-type aftershock sequence model. This approach is tested on case studies of the 2013 Bushehr, 2021 and 2022 Hormozgan seismic events, and characterizes the aftershocks’ hierarchical structure to improve the reliability of aftershock hazard assessments. Then, the results of APSHA (aftershock ground-motion hazard at specific sites) based on smoothing are compared with conventional PSHA (pre-mainshock ground-motion hazard at specific sites). This comparative analysis highlights the importance of considering aftershock effects when assessing ground-motion hazards because PSHA does not fully account for aftershock hazard increases following major earthquakes.
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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 | 0.002 | 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 | 0.001 | 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".