Ground Deformation in Yuxi Basin Based on Atmosphere-Corrected Time-Series InSAR Integrated with the Latest Meteorological Reanalysis Data
Bibliographic record
Abstract
Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".