The<i>M</i>7 2016 Kumamoto, Japan, Earthquake: 3‐D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography
Bibliographic record
Abstract
Abstract Three‐dimensional near‐fault coseismic deformation fields from high‐resolution differential topography provide new information on the behavior of the shallow fault zone in large surface‐rupturing earthquakes. Our work focuses on the 16 April 2016 M w 7.0 Kumamoto, Japan, earthquake, which ruptured ~40 km of the Futagawa‐Hinagu Fault Zone on Kyushu Island with an oblique strike‐slip mechanism and surface offset exceeding 2 m. Our differential lidar analysis constrains the structural style of strain accommodation along the primary fault trace and the surrounding damage zone. We show that 36 ± 29% and 62 ± 32% of the horizontal and vertical deformation, respectively, was accommodated off the principal fault trace. The horizontal strains of up to 0.03 suggest that the approximate elastic strain limit was exceeded over a ~250 m width in many locations along the rupture. The inelastic deformation of the fault volume produced the observed distributed deformation at the Earth's surface. We demonstrate a novel approach for calculating 3‐D displacement uncertainties, indicating errors of centimeters to a few decimeters for displacements computed over 50 m horizontal windows. Errors correlate with land cover and relief, with flatter agricultural land associated with the highest displacement uncertainty. These advances provide a framework for future analyses of shallow earthquake behavior using differential topography.
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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 | 0.000 |
| 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 | 0.000 |
| 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".