An Integrated Earthquake Catalog for Aotearoa New Zealand (Version 1), Event-Type Classifications, and Regional Earthquake Depth Distributions
Bibliographic record
Abstract
ABSTRACT We compile an integrated earthquake catalog for Aotearoa New Zealand (NZ) by overwriting event parameters in the national operational seismic catalog (the most complete record of NZ’s seismicity) with refined estimates of event depths, focal mechanisms, locations, and magnitudes from other sources. This was required for several uses in the 2022 NZ National Seismic Hazard Model (NZ NSHM 2022), including distinguishing (classifying) upper-plate, subduction-interface, and intraslab earthquakes to guide the statistics of separate components of the NZ NSHM 2022’s Seismicity Rate Model. Starting from a branch of the operational catalog with standardized event magnitudes, we import revised parameters for 60% of the catalog (including 92% of all 2000–2020 events, 89% of 1951–2020 M ≥ 5.5 events and 84% of 1917–2020 M ≥ 6 events) from relocation studies, literature, the NZ Centroid Moment Tensor database and global catalogs. Next, we classify earthquakes as upper plate, subduction, or intraslab by comparing their depths, locations, and focal mechanisms to the Hikurangi–Kermadec and Puysegur subduction interface geometries and relative plate-motion directions. We show that this event classification would be either highly error-prone or effectively blind in subduction regions if the catalog had not been revised beforehand. Finally, we estimate the depth distribution of upper-plate earthquakes in multiple regions for use in the NZ NSHM 2022 and explore some post-2022 developments of this 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".