The InSAR lookbook: an illustrated guide to earthquake interferograms
Bibliographic record
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is the prevalent method for mapping earthquake deformation and is seeing ever-increasing popularity through a new generation of satellite missions. Nowadays, following any large onshore earthquake, InSAR images (interferograms) are quickly disseminated across the community and media, but outside of InSAR specialists there remains a lack of general understanding of how to interpret them. We begin our study by describing how InSAR fringe patterns are determined by the combination of horizontal and vertical ground motions and ascending or descending satellite viewing geometries. In our "lookbook", we synthesize interferograms for a comprehensive suite of faulting styles, including strike-slip, reverse, normal, low-angle thrust, low-angle normal, and oblique-slip faults. This highlights the most common InSAR fringe patterns and demonstrates how strike-slip, dip-slip, and oblique-slip earthquakes produce distinct fringe patterns controlled primarily by their strike angles. We offer guidelines for utilizing the lookbook to assess earthquake mechanisms visually and to pick the causative fault plane from two nodal planes. Lastly, by comparing modelled interferograms and real-world earthquakes, we showcase the broad applicability of the lookbook, even for complex multiple segment ruptures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.114 |
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".