Three‐Dimensional Surface Displacements During the 2016 <i>M</i><sub><i>W</i></sub> 7.8 Kaikōura Earthquake (New Zealand) From Photogrammetry‐Derived Point Clouds
Bibliographic record
Abstract
Abstract High‐resolution, three‐dimensional (3‐D) measurements of surface displacements during earthquakes can provide constraints on fault geometry and near‐surface slip and also quantify on‐fault and off‐fault deformation. However, measurements of surface displacements are often hampered by a lack of high‐resolution preearthquake elevation data, such as lidar. For example, preearthquake lidar for the 2016 M W 7.8 Kaikōura, New Zealand, earthquake only covers ≲10% of ~180 km of mapped surface ruptures. To overcome a lack of preearthquake lidar, we measure 3‐D coseismic displacements during the Kaikōura earthquake using point clouds generated from aerial photographs. From these point clouds, which cover the whole area of the 2016 surface ruptures, it is possible to measure 3‐D displacements to within ±0.2 m. We measure coseismic slip and estimate the geometries of faults in the steep, inaccessible Seaward Kaikōura mountains, where postearthquake field observations are very sparse. The Jordan Fault (previously the Jordan Thrust) ruptured in 2016 as a moderate‐to‐steeply dipping (~50–80°), predominantly strike‐slip fault. Slip on this fault in 2016—which included a normal‐sense component in some areas—contrasts with field observations that indicate significant longer‐term shortening across the Jordan Fault during the Holocene. It is therefore likely that different earthquakes on the Jordan Fault have very different slip vectors and that the fault does not exhibit “characteristic” slip behavior. For faults like the Jordan Fault, long‐term time‐averaged estimates of slip rate may not be reliable indicators of the sense and magnitude of slip in individual 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".