USE OF GROUND MOTION SIMULATIONS OF MEGATHRUST EVENTS IN CAT MODELLING FOR THE INSURANCE INDUSTRY
Bibliographic record
Abstract
Several densely populated areas around the world are located in proximity to subduction zones and their hazard and risk are largely driven by large megathrust events, such as Chile and the Pacific Northwest, USA and Western Canada regions along the Cascadia Subduction zone. Ground shaking and loss estimation in catastrophe risk models conventionally relies on regression of empirical ground motion recordings (Ground Motion Prediction Equations, or GMPEs) from historical earthquakes. However, GMPEs tend to produce large uncertainty due to the inherent smoothing, and potential bias in the estimated ground motions, particularly for large, rare megathrust events. The 1960 M9.5 Valdivia earthquake in the Nazca Subduction zone was insufficiently recorded by seismic instrumentation and since not many large magnitude megathrust earthquakes have been recorded globally, GMPEs are mostly unconstrained for such large-magnitude events. This study explores the use of more sophisticated physics-based simulations within the context of portfolio risk modelling for the reinsurance industry, a viable alternative to using GMPEs for ground shaking and loss estimation from megathrust events. The study illustrates the use in catastrophe modelling of earthquake footprints developed by San Diego State University (SDSU) using advanced 3D ground motion simulation techniques for megathrust scenarios in the South America and Cascadia subduction zones. A comparison of earthquake footprints and resulting loss estimated using GMPEs (currently used in catastrophe models) and state-of-the-art physics-based ground motion simulations highlights deficiencies of the conventional approach particularly in Santiago, Seattle and Vancouver where the 3D effects of the sedimentary basin can strongly affect the resulting ground motion predictions and loss estimation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".