Stochastic Analysis of Tsunami Hazard of the 1945 Makran Subduction Zone Mw 8.1–8.3 Earthquakes
Bibliographic record
Abstract
Historical records of major earthquakes in the northwestern Indian Ocean along the Makran Subduction Zone (MSZ) indicate high potential tsunami hazards for coastal regions of Pakistan, Iran, Oman, and western India. There are fast-growing and populous cities and ports that are economically important, such as Chabahar (Iran), Gwadar (Pakistan), Muscat (Oman), and Mumbai (India). In this study, we assess the tsunami hazard of the 1945 MSZ event (fatalities ≈300 people) using stochastic earthquake rupture models of Mw 8.1–8.3 by considering uncertainties related to rupture geometry and slip heterogeneity. To quantify the uncertainty of earthquake source characteristics in tsunami hazard analysis, 1000 stochastic tsunami scenarios are generated via a stochastic source modeling approach. There are main objectives of this study: (1) developing stochastic earthquake slip models for the MSZ, (2) comparing results of the simulation with the existing observations of the 1945 event, and (3) evaluating the effect of uncertain fault geometry and earthquake slip based on simulated near-shore wave profiles. The 1945 Makran earthquake is focused upon by comparing model predictions with existing observations, consisting of far-field tsunami waveforms recorded on tide gauges in Karachi and Mumbai and coseismic deformation along the Pakistani coast. The results identify the source model that matches the existing observations of the 1945 Makran event best among the stochastic sources. The length, width, mean slip, and maximum slip of the identified source model are 270 km, 130 km, 2.9 m, and 19.3 m, respectively. Moreover, the sensitivity of the maximum tsunami heights along the coastline to the location of a large-slip area is highlighted. The maximum heights of the tsunami and coseismic deformation results at Ormara are in the range of 0.3–7.0 m and −2.7 to 1.1 m, respectively, for the 1000 stochastic source models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".