Study of the Dissimilar Deformation of Huxian, Near the Qinling Piedmont Fault (China), by Multiband InSAR Time-Series Technology
Bibliographic record
Abstract
In recent years, small earthquakes around Huxian, near the Qinling Piedmont Fault (China), have attracted much attention from the local government. Due to the blindness to the characteristics of Huxian ground deformation, it is highly desirable to conduct ground deformation surveys in this area, as well as study the relationship between ground deformation and these earthquakes. To understand the surface-deformation characteristics of Huxian and the seismic background better, InSAR time-series processing was applied to C-, L-, and X-band SAR images of Huxian. InSAR results show that the surface motion was uplifting from 2007 to 2010, and the surface motion subsided from 2014 to 2015. The 2-dimensional deformation rate field from 2007 to 2010 was obtained by using ascending L-band images and descending C-band images. Models in elastic half-space were used to invert the depth of the surface-deformation power source, the results of which are less than 2.0 km below the ground surface. The inversion results were compared with the available seismic records. Possible reasons for the surface deformation are discussed. According to our study, the observed movement has weak correlation with earthquakes even though the small earthquake activity in this area increased suddenly during recent years.
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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 | 0.002 |
| 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 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".