Fault displacement accumulation and slip rate variability within the Taupo Rift (New Zealand) based on trench and 3‐D ground‐penetrating radar data
Bibliographic record
Abstract
In offshore regions, studies based on densely spaced reflection seismic data tied to stratigraphic logs demonstrate that active faults can have variable displacement rates over relatively short distances and short time intervals. Here, we demonstrate how high‐resolution 3‐D ground‐penetrating (GPR) data tied to trench‐derived stratigraphic logs provide similar information for active faults in onshore regions. To investigate recent (≤24.4 ka) fault activity within the Taupo Rift of New Zealand, we analyze 3‐D GPR data acquired over 10 fault strands within the Maleme fault zone. After correlating three prominent GPR reflection horizons with three faulted chronostratigraphic units observed within a trench, we extrapolate the geometries of the horizons over a ∼150 × 250 m area of the fault zone and determine slip accumulation patterns and rates. Profiles of cumulative fault displacement measured for horizons older than 12.5 ka exhibit characteristic displacement distributions. By calculating average cumulative displacements with time for five practically complete fault strands, we obtain robust slip rate estimates. Slip rates are variable for time intervals ≤12.5 ka long, suggesting that at least four earthquakes are required for these faults to exhibit uniform slip rates characteristic of their long‐term behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".