Predicted Ground Motions for Great Interface Earthquakes in the Cascadia Subduction Zone
Bibliographic record
Abstract
Ground motions for earthquakes of moment magnitude ( M ) 7.5–9.0 on the Cascadia subduction zone interface are simulated based on a stochastic finite-fault model and used to estimate average response spectra for firm-site conditions in Vancouver, Victoria, and Seattle. We also express the response spectra as ground-motion prediction equations (GMPEs) for Cascadia events. The simulations are calibrated by modeling the wealth of ground-motion data from the M 8.1 Tokachi-Oki earthquake sequence of Japan. Adjustments to the calibrated model are made to consider average source, attenuation, and site parameters for the Cascadia region. We perform best estimate simulations for a preferred set of input parameters. Typical results suggest mean values of 5%-damped pseudoacceleration in the range from about 100 to 200 cm/sec2, at frequencies from 1 to 4 Hz, for firm-ground conditions in Vancouver, Victoria, and Seattle. Uncertainty in stress drop causes uncertainty in simulated response spectra of about ±50%. Uncertainties in the attenuation model produce even larger uncertainties in response spectral amplitudes—a factor of about 2 at 100 km, becoming even larger at greater distances. It is thus important to establish the regional attenuation model for ground-motion simulations. Furthermore, combining data from regions with different attenuation characteristics—in particular Japan and Mexico—into a global subduction zone database for development of global empirical GMPEs may not be a sound practice. Time histories of acceleration for the stochastically simulated motions are provided for reference sites in Vancouver, Victoria, and Seattle. An alternative set of motions, based on lightly modifying real recordings from the Tokachi-Oki earthquake to match expected conditions for Cascadia cities, are also provided. These alternative records have similar spectral content to the simulated motions but contain additional complexity and more realistic phasing. The provision of alternative record sets allows users to conduct studies to determine the importance of these effects for structural response.
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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 | 0.002 |
| 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 | 0.000 | 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".