A regionalized partially nonergodic ground‐motion model for subduction earthquakes using the NGA‐Sub database
Bibliographic record
Abstract
In this study, we derived a regionalized partially nonergodic empirical ground‐motion model (GMM) for subduction interface and intraslab earthquakes using an extensive global database compiled as part of the NGA‐Subduction project. The model can be used to estimate peak ground acceleration (PGA), peak ground velocity (PGV), and ordinates of 5%‐damped pseudo‐spectral acceleration (PSA) at periods ranging from 0.01 to 10 s for M ≥ 5.0, M ≤ 8.5 for intraslab events, M ≤ 9.5 for interface events, Z TOR ≤ 50 km for interface events, Z TOR ≤ 200 km for intraslab events, 10 ≤ R RUP ≤ 800 km, and 100 ≤ V S 30 ≤ 1000 m/s. Besides a global version of the model, the GMM accounts for regional differences in the overall amplitude (constant), anelastic attenuation, linear site response, and basin response for seven subduction‐zone regions: Alaska (AK), Central America and Mexico (CAM), Cascadia (CASC), Japan (JP), New Zealand (NZ), South America (SA), and Taiwan (TW). The functional form of the model is structured such that the breakpoint magnitude, the magnitude at which the magnitude‐scaling rate (MSR) transitions from a steeper to a shallower slope, is an adjustable parameter in the model. This makes it possible to take epistemic uncertainty in this parameter into account or adjust it based on other empirical or physical information, such as when the model is applied to a subduction zone not considered in the GMM. Besides the traditional mixed‐effects aleatory between‐event standard deviations and within‐event standard deviations, within‐model epistemic standard deviations in the median prediction for each region is quantified from a posterior distribution of model coefficients, standard deviations, and coefficient correlations using a Bayesian regression approach. Our full 800‐sample posterior distribution can be used to account for epistemic uncertainty in the model coefficients, standard deviations, and predicted values. We also provide a simplified epistemic model using magnitude‐ and distance‐dependent within‐model standard deviations that can be used to facilitate the inclusion of within‐model epistemic uncertainty directly in a probabilistic seismic hazard analysis. The within‐model standard deviations can also be used to scale the GMM using a backbone modeling approach.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".