Are Aftershock Sequences Pertinent to Long‐Term Seismic Hazard Assessments? Insights From the Temporal ETAS Model
Bibliographic record
Abstract
Abstract Aftershocks are commonly removed from observed earthquake catalogs in probabilistic seismic hazard analyses, using declustering techniques. We use stationary and temporal Epistemic‐Type Aftershock Sequence (ETAS) models to generate aftershocks from background seismicity and preceding aftershocks. We assume that the mainshocks equal the background seismicity, to divide the synthetic earthquake catalogs into mainshock (declustered) and complete (nondeclustered) versions. Only mainshocks follow a Poissonian distribution. We then evaluate how accurately we can forecast the recurrence of the largest events based on the simulated catalogs. A single b value is derived from the simulated catalogs and used in the magnitude‐frequency forecast. When the b value of the mainshocks is considerably smaller than the b value of the aftershocks, the information derived from the mainshock catalog leads to accurate predictions of the occurrence of the largest events. Conversely, when the mainshock and aftershocks have comparable b values, only the complete catalog produces representative estimates for the occurrence statistics of the largest events. We also show that using Poisson statistics leads to representative assessment of long‐term recurrences, even if aftershocks have a non‐Poissonian distribution. Finally, we analyze a recent case of induced seismicity, Oklahoma, USA, where the complete catalog displays a kink in the magnitude‐frequency distribution. Declustering removes this kink, leading to better b value estimations for the largest magnitude events. We conclude that temporal declustering for seismic hazard assessment is only recommended in catalogs with a large number of earthquakes and in catalogs where the b values of the mainshocks are significantly different from the b values of the complete catalog.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".