NGA‐West2 Equations for Predicting Vertical‐Component PGA, PGV, and 5%‐Damped PSA from Shallow Crustal Earthquakes
Bibliographic record
Abstract
We present ground motion prediction equations (GMPEs) for computing natural log means and standard deviations of vertical‐component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. The functions are similar to those for our horizontal GMPEs. We derive equations for the primary M ‐ and distance‐dependence of peak acceleration, peak velocity, and 5%‐damped pseudo‐spectral accelerations at oscillator periods between 0.01–10 s. We observe pronounced M ‐dependent geometric spreading and region‐dependent anelastic attenuation for high‐frequency IMs. We do not observe significant region‐dependence in site amplification. Aleatory uncertainty is found to decrease with increasing magnitude; within‐event variability is independent of distance. Compared to our horizontal‐component GMPEs, attenuation rates are broadly comparable (somewhat slower geometric spreading, faster apparent anelastic attenuation), V S 30 ‐scaling is reduced, nonlinear site response is much weaker, within‐event variability is comparable, and between‐event variability is greater.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".