Evaluation of Empirical Ground-Motion Models for the 2022 New Zealand National Seismic Hazard Model Revision
Bibliographic record
Abstract
ABSTRACT This article presents an evaluation of empirical ground-motion models (GMMs) for active shallow crustal, subduction interface, and subduction slab earthquakes using a recently developed New Zealand (NZ) ground-motion database for the 2022 New Zealand National Seismic Hazard Model revision. This study considers both NZ-specific and global models, which require evaluation to inform of their applicability in an NZ context. A quantitative comparison between the models is conducted based on intensity measure residuals and a mixed-effects regression framework. The results are subsequently investigated to assess how the models are performing in terms of overall accuracy and precision, as well as to identify the presence of any biases in the model predictions when applied to NZ data. Many models showed reasonable performance and could be considered appropriate for inclusion within suites of models to properly represent ground-motion predictions and epistemic uncertainty. In general, the recent models that are NZ-specific or developed on large international databases performed the best. This evaluation of models helped inform suitable GMMs for the ground-motion characterization model logic tree. In addition, spatial trends in systematic site-to-site residuals to the west of the Taupō Volcanic Zone demonstrated the need for backarc attenuation modifications for slab earthquakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".