Using Phase Unwrapping Methods to Apply D-InSAR in Mining Areas
Bibliographic record
Abstract
Phase unwrapping is one of the key processing steps of D-InSAR (Differential Interferometric Synthetic Aperture Radar), and its accuracy has a great impact on the accuracy of interferometric deformation results. Therefore, it is necessary to choose the proper phase unwrapping method for mining deformation monitoring with different coherence by the comparative study of different phase unwrapping methods, because the coherence is different for different mining areas. In this paper, 2 data sets of different coherence Sentinel-1A data in Xuangang mining areas were processed using 3 different D-InSAR unwrapping methods: Region Growing, Minimum Cost Flow, and Delaunay MCF by SARscape software. Through statistical analysis of phase unwrapping results and comparative analysis of the difference between the theoretical deformation value and the deformation value calculated by SARscape, the Delaunay MCF unwrapping method is found to be the best to monitor low-coherence mining areas under most conditions. However, the Minimum Cost Flow method is a better choice when the coherence of mining areas is high, and smoother and more continuous phase unwrapping results can be obtained by selecting a mining area with better coherence.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".