Large deformation monitoring over a coal mining region using pixel-tracking method with high-resolution Radarsat-2 imagery
Bibliographic record
Abstract
Differential synthetic aperture radar interferometry (D-InSAR) is limited when exploited in high-intensity mining areas, because large deformation gradients lie beyond the maximum measurable value of the D-InSAR technique which breaks the prerequisite for successfully employing of the method. The SAR amplitude-based pixel-tracking method provides an alternative way to efficiently and robustly extract the large deformation distribution particularly when the D-InSAR technique is limited by loss of coherence. In addition, the deformation in the line-of-sight direction and the deformation along the azimuth direction are also presented in this paper with 24-day interval repeat-pass high-resolution Rardarsat-2 imagery. Combining both of these techniques can help to better understand the deformation mechanisms associated with underground mining activities. The accuracies of 0.12 m in slant-range direction and 0.19 m in the azimuth direction were achieved, respectively. Besides, the profiles across the maximum deformation region have verified that the deformation occurred during two acquisition periods is far beyond the theoretical maximum deformation gradient corresponding to high-resolution C-band SAR data. The obtained surface motion infers to the mining activities and assessed damage caused by the large deformation.
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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) | 0.000 | 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".