Determination of the dipping direction of a blind reverse fault from InSAR: case study on the 2017 Sefid Sang earthquake, northeastern Iran
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Bibliographic record
Abstract
Abstract Determining the fault parameters of an earthquake is fundamental for studying the earthquake physics, understanding the seismotectonics of the region, and forecasting future earthquake activities in the surrounding area. Dense crustal deformation data such as Interferometric Synthetic Aperture Radar (InSAR) are useful for fault parameter determination, but determining the dipping direction of a blind fault is often challenging when the size of the earthquake is not large ( M < 7) or when the coverage of the deformed area is limited to capture the details of rupture. The 5th April 2017, Mw 6.1 earthquake occurred near the city of Sefid Sang, northeast of Iran, provides an excellent case for exploring the potential of InSAR data for determining the dipping direction of a blind reverse fault. Using Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1A interferograms of four different observation directions and a fault slip inversion method that allows thorough exploration of the fault geometry led to two candidates of reverse fault models, dipping either to the northeast or the south. The results show that the fault models of both dipping directions explain the data well, with a slight advantage in the northeast-dipping fault model in terms of the misfit when the atmospheric corrections were applied. The northeast-dipping fault model is, in addition, more consistent with the strike of the mapped active faults in the region and with the aftershock distribution, from which we infer that the 2017 Sefid Sang earthquake occurred on a northeast-dipping dextral-reverse fault. The preferred fault model has a strike angle of 314.8°, dip angle of 47.4° and rake angle of 130.3°, and a slip distribution of maximum 1.35 m at depth of 5 km equivalent to Mw 6.0. This study illuminates the difficulty of determining the dipping direction of blind faults even with InSAR measurements from multiple directions, but also that correcting for the atmospheric noise and comparing with other kinds of data can help infer the fault dipping direction.
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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 | 0.000 | 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) | 0.000 | 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 it