On Dislocation Modeling of Megathrust Tsunami Sources
Bibliographic record
Abstract
Abstract Modeling tsunamis due to subduction earthquakes for scientific research and hazard assessment requires accurate quantification of coseismic seafloor deformation. Although the widely used analytical model of shear dislocation in a uniform elastic half space can accommodate complex fault geometry and slip distribution, it fails to capture the sloping seafloor topography and heterogeneous rock rigidity (shear modulus) in real subduction zones. In practice, these real‐world complexities are either ignored or addressed by adjusting fault geometry and/or applying corrections to the deformation results, with consequences poorly understood. This study investigates the validity or errors of these simplifications by comparing dislocation model results with those from finite element models that account for these complexities. Our analysis reveals that the absence of the seafloor slope can be accurately compensated by adjusting the shallow geometry of the megathrust such that the fault depth below the flat model surface approximates the actual fault depth below the seafloor. Effects of short‐wavelength bathymetry can be effectively incorporated by adding a commonly used gradient‐based correction. For slip‐to‐trench ruptures, it is crucial to adjust fault geometry to ensure that the fault reaches the model surface at the trench; otherwise, the abrupt slip termination at a small depth creates an uplift spike which is a commonly seen artifact in tsunami source models. Our findings highlight the secondary or minimal effects of heterogeneous rigidity on tsunamigenic deformation if fault slip is kinematically assigned. This research offers guidance for the development of more accurate tsunami source models using analytical dislocation solution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".