Submeter Resolution Surface Rupture Topography From Legacy Aerial Photographs—A Test Case From the 1992 Landers Earthquake
Bibliographic record
Abstract
Abstract The 1992 7.3 Landers earthquake in the Mojave Desert (California) provided exceptional observations of surface faulting in a large, continental earthquake. The U. S. Geological Survey obtained nadir angle, overlapping aerial photographs at 1:6,000 scale for the entire 85 km rupture length. Recent advances in Structure from Motion photogrammetry allow for archival photographic data sets such as these to be reprocessed, generating digital topography that can be reanalyzed quantitatively in a way that was not previously possible. In this proof‐of‐concept study, we generated a georectified, 10 points/m topographic point cloud over nearly the entire Landers rupture length and a higher‐resolution 40 points/m point cloud over a smaller ( 5 km) rupture segment along the Emerson fault. We estimated the accuracy and explore the utility of our point cloud in two tests. First, we observe close geometric agreement (average closest point distance 2.1 cm and standard deviation 14.0 cm) between our point cloud and a 2008 terrestrial lidar survey of the Galway Lake Road site on the Emerson fault. Second, we made 173 vertical offset measurements within a small, structurally complex pull‐apart basin, also on the Emerson fault, and find visual and statistical similarity with 21 local field measurements. These two tests demonstrate that point clouds generated from legacy aerial surveys and georeferenced using free Google Earth and National Elevation Dataset imagery are geometrically accurate and can be used to densify geomorphic offset measurements even along well‐studied surface ruptures. Applied to other historical events, such measurements could provide new insights into earthquake rupture processes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".