Probabilistic tsunami hazard assessment for the makran subduction zone using logic tree and stochastic rupture sources
Bibliographic record
Abstract
The Makran Subduction Zone (MSZ) in the northwestern Indian Ocean can generate large tsunamigenic thrust earthquakes affecting the coastal regions of Pakistan, Iran, Oman, and western India. In this paper, a probabilistic tsunami hazard assessment is conducted for the MSZ using stochastic tsunami simulations of moment magnitude (Mw) of 7.7–9.1 earthquake scenarios. This study investigates uncertainties associated with earthquake occurrence rate, single-segment (eastern and western MSZ) or two-segment (full MSZ) rupture scenarios, source geometry, and slip heterogeneity. The total number of simulated source models is 15,000. This study presents two major categories of results: stochastic source models and ranges of 475, 975, and 2475-year tsunami heights. For instance, tsunami heights generated by Mw 8.5‒8.7 stochastic sources of western MSZ vary between 1 m and 10 m with a mean of ~ 4.5 m in the affected areas. The tsunami heights are sensitive to the source models’ characteristics, such as location of the large slip areas, bathymetry of the nearshore area, and the location of bays. Considering different occurrence rates results in significant variability in the estimated 475, 975, and 2475-year tsunami heights. For example, 2475-year tsunami height in Chabahar is in the range of 3‒7.4 m at 10 m water depth.
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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) | 0.000 | 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 | 0.000 |
| 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".