Overview of Site Effects and the Application of the 2022 New Zealand NSHM in the Wellington Basin, New Zealand
Bibliographic record
Abstract
ABSTRACT We provide an overview of the treatment of site effects in the New Zealand National Seismic Hazard Model (NZ NSHM), including a case study of basin effects in central Wellington. The NZ NSHM 2022 includes a change in site parameter from subsoil class (NZS class) to VS30. Poor NZ VS30 characterization is a major source of uncertainty in the NSHM; however, advanced site characterization in Wellington allows for in-depth study. First, we construct a regional 3D shear-wave velocity model and maps of site parameters (T0, NZS class, and VS30) for central Wellington. At central city soil sites, we find the ratios of NZ NSHM 2022 hazard spectra with respect to the current equivalent design spectra range from factors of ∼0.8–2.6 (median ∼1.5), depending on local site conditions and spectral period. Strong amplification peaks at 0.5–2 s are observed in central Wellington. Linear site-specific amplifications from multiple methods are compared at 13 stations and are well-defined by both site-to-site residuals and response spectral ratios relative to station POTS. At many deeper soft sites (VS30<300 m/s), strong amplification peaks occur around T0 that are underpredicted by mean ergodic ground-motion model (GMM) predictions. This underprediction is slightly enhanced when using basin-specific Z1.0 as an additional site parameter. Our study highlights outstanding challenges in modeling strong basin response within shallow basins in NSHMs, including the need to consider region- or basin-specific modeling approaches as well as nonlinear effects at high shaking intensities that dominate the hazard. For New Zealand, in general, as illustrated in the Wellington case study, a priority is the further characterization of VS30 (and VS) for the seismic network to better isolate and quantify uncertainties in seismic hazard and allow useful exploration of regional–GMM adjustments and partially nonergodic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".