Performance of Slab Geometry Constraints on Rapid Geodetic Slip Models, Tsunami Amplitude, and Inundation Estimates in Cascadia
Bibliographic record
Abstract
Tsunamigenic megathrust earthquakes along the Cascadia subduction zone present a major hazard concern. We can better prepare to model the earthquake source in a rapid manner by imbuing fault geometry constraints based on prior knowledge and by evaluating the capabilities of using existing GNSS sensors. Near-field GNSS waveforms have shown promise in providing rapid coarse finite-fault model approximations of the earthquake rupture that can improve tsunami modeling and response time. In this study, we explore the performance of GNSS derived finite-fault inversions and tsunami forecasting predictions in Cascadia that highlights the impact and potential of geodetic techniques and data in operational earthquake and tsunami monitoring. We utilized 1300 Cascadia earthquake simulations (FakeQuakes) that provide realistic (M7.5-9.3) rupture scenarios to assess how feasibly finite-fault models can be obtained in a rapid earthquake early warning and tsunami response context. A series of fault models with rectangular dislocation patches spanning the Cascadia megathrust area is added to the GFAST inversion algorithm to calculate slip for each earthquake scenario. Another method used to constrain the finite-fault geometry is from the GNSS-derived CMT fault plane solution. For the Cascadia region, we show that fault discretization using two rectangular segments approximating the megathrust portion of the subduction zone leads to improvements in modeling magnitude, fault slip, tsunami amplitude, and inundation. In relation to tsunami forecasting capabilities, we compare coastal amplitude predictions spanning from Vancouver Island (Canada) to Northern California (USA). Generally, the coastal amplitudes derived using fault parameters from the CMT solutions show an overestimation bias compared to amplitudes derived from the fixed slab model. We also see improved prediction values of the run-up height and maximum amplitude at 10 tide gauge stations using the fixed slab model as well.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".