Source parameters of the 2017<i>M</i>w 6.2 Yukon earthquake doublet inferred from coseismic GPS and ALOS-2 deformation measurements
Bibliographic record
Abstract
We investigated an Mw ∼ 6.2 earthquake doublet on the border of the USA and Canada using ALOS2 Light-of-Sight displacements and GPS measurements. We selected three L-band ALOS-2 interfergorams with temporal baselines of one yr to extract coseismic deformation maps, in which master and slave images were both acquired in July. A subpixel-based alignment and another range spectral splitting techniques under the GAMMA InSAR software framework were applied to improve the interferometric coherence and reduce the effects of phase anomalies in two of the three interferometric pairs due to either ionospheric delay or a potential focusing issues in the generation of the ALOS2 SLC data. The updated interferograms convincingly reveal deformation fringe patterns produced by the two earthquakes. We conducted a nonlinear geophysical inversion to estimate the geometric parameters of the earthquakes with the InSAR and GPS measurements. The best-fitting model shows that a thrust faulting on a reverse fault and left-lateral strike-slip faulting on a nearly vertical fault with the centroid depths of 9.3±0.6 and 8.4±0.7 km, respectively, are most likely responsible for the earthquake doublet. The eastern Denali fault (EDF) and Duke River fault are major active faults in the region and the earthquake doublet could be due to reactivation of the part of the two faults system.
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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 | 0.001 | 0.001 |
| 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".