Variability of ETAS Parameters in Global Subduction Zones and Applications to Mainshock–Aftershock Hazard Assessment
Bibliographic record
Abstract
ABSTRACT Megathrust earthquake sequences can impact buildings and infrastructure due to not only the mainshock but also the triggered aftershocks along the subduction interface and in the overriding crust. To give realistic ranges of aftershock simulations in regions with limited data and to provide time-dependent seismic hazard information right after a future giant shock, we assess the variability of the epidemic-type aftershock sequence (ETAS) model parameters in subduction zones that have experienced M≥7.5 earthquakes, comparing estimates from long time windows with those from individual sequences. Our results show that the ETAS parameters are more robust if estimated from a long catalog than from individual sequences, given individual sequences have fewer data including missing early aftershocks. Considering known biases of the parameters (due to model formulation, the isotropic spatial aftershock distribution, and finite size effects of catalogs), we conclude that the variability of the ETAS parameters that we observe from robust estimates is not significant, neither across different subduction-zone regions nor as a function of maximum observed magnitudes. We also find that ETAS parameters do not change when multiple M 8.0–9.0 events are included in a region, mainly because an M 9.0 sequence dominates the number of events in the catalog. Based on the ETAS parameter estimates in the long time period window, we propose a set of ETAS parameters for future M 9.0 sequences for aftershock hazard assessment (K0=0.04±0.02, α=2.3, c=0.03±0.01, p=1.21±0.08, γ=1.61±0.29, d=23.48±18.17, and q=1.68±0.55). Synthetic catalogs created with the suggested ETAS parameters show good agreement with three observed M 9.0 sequences since 1965 (the 2004 M 9.1 Aceh–Andaman earthquake, the 2010 M 8.8 Maule earthquake, and the 2011 M 9.0 Tohoku earthquake).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".