Simulated tsunami inundation for a range of Cascadia megathrust earthquake scenarios at Bandon, Oregon, USA
Bibliographic record
Abstract
Characterizations of tsunami hazards along the Cascadia subduction zone hinge on uncertainties in megathrust rupture models used for simulating tsunami inundation.To explore these uncertainties, we constructed 15 megathrust earthquake scenarios using rupture models that supply the initial conditions for tsunami simulations at Bandon, Oregon.Tsunami inundation varies with the amount and distribution of fault slip assigned to rupture models, including models where slip is partitioned to a splay fault in the accretionary wedge and models that vary the updip limit of slip on a buried fault.Constraints on fault slip come from onshore and offshore paleoseismological evidence.We rank each rupture model using a logic tree that evaluates a model's consistency with geological and geophysical data.The scenarios provide inputs to a hydrodynamic model, SELFE, used to simulate tsunami generation, propagation, and inundation on unstructured grids with <5-15 m resolution in coastal areas.Tsunami simulations delineate the likelihood that Cascadia tsunamis will exceed mapped inundation lines.Maximum wave elevations at the shoreline varied from ~4 m to 25 m for earthquakes with 9-44 m slip and M w 8.7-9.2.Simulated tsunami inundation agrees with sparse deposits left by the A.D. 1700 and older tsunamis.Tsunami simulations for large (22-30 m slip) and medium (14-19 m slip) splay fault scenarios encompass 80%-95% of all inundation scenarios and provide reasonable guidelines for landuse planning and coastal development.The maximum tsunami inundation simulated for the greatest splay fault scenario (36-44 m slip) can help to guide development of local tsunami evacuation zones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".