Time-dependent Probabilistic Tsunami Hazard Analysis for Western Sumatra, Indonesia, Using Space-Time Earthquake Rupture Modelling and Stochastic Source Scenarios
Bibliographic record
Abstract
Abstract. We develop a novel framework of time-dependent probabilistic tsunami hazard analysis (PTHA) and apply it to Western Sumatra, Indonesia, where future tsunamigenic events are anticipated in the Mentawai region of the Sunda subduction zone. An earthquake rupture model taking into account the spatiotemporal interaction of major megathrust segments is used to simulate future tsunamigenic earthquakes. The earthquake rupture process of the segments is characterized by a multivariate Bernoulli model with interarrival times following a Brownian passage‐time distribution and the dependency between segments specified by a spatial correlation function. We calibrate this model with historical ruptures of the Mentawai thrust in the last 450 years. A total of ≥ 100,000 time-dependent earthquake rupture cases are then coupled with a stochastic tsunami simulation method to evaluate tsunami hazards. We generate a total of 6,300 stochastic tsunami source models from six magnitude scenarios between M 7.75 and M 9.0 and obtain time-dependent PTHA results for seven different periods (1, 5, 10, 20, 30, 50 and 450 years). We further compare the time-dependent PTHA results with a time-independent PTHA approach to investigate the influence of the spatiotemporal earthquake rupture model. The space-time interaction model successfully generates annual seismic moment rates consistent with the observations. Moreover, the model can capture the uncertainty of future time-dependent tsunami hazards. On the other hand, the time-independent approach produces slightly higher hazard estimates than the time-dependent model for long-term hazard assessments (> 450 years).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".