Backbone Ground-Motion Models for Crustal, Interface, and Slab Earthquakes in New Zealand from Equivalent Point-Source Concepts
Bibliographic record
Abstract
ABSTRACT A ground-motion model (GMM) that strikes a balance between empirical and simulation-based approaches is developed in support of the 2022 update of the New Zealand National Seismic Hazard Model. The development follows the backbone approach, comprising a central model to express the median ground motions for earthquakes in New Zealand (NZ), along with upper and lower alternatives to describe its epistemic uncertainty. Aleatory variability of ground-motion amplitudes about the median is also characterized. Separate GMMs are developed for crustal, interface, and in-slab earthquakes. The approach taken is to perform a regression analysis of the NZ response spectra database employing a functional form, concepts, and constraints that are drawn from equivalent point-source simulations. The model parameters that control the scaling of the GMM with magnitude and distance describe source effects (seismic moment and stress parameter), path effects (geometric and anelastic attenuation), and site effects (site shear-wave velocity). The NZ database provides constraints on the model for M ∼ 4–7, for frequencies from 0.2 to 100 Hz, at distances to ∼400 km. Extension of the GMM to larger magnitudes (M 7–9) is constrained by the Hassani and Atkinson seismological model, which was developed for application to events of M 3–9 and validated in data-rich regions (California for crustal earthquakes, Japan for interface and slab earthquakes).
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".