InSAR-based mapping of surface subsidence in Mokpo City, Korea, using JERS-1 and ENVISAT SAR data
Bibliographic record
Abstract
Abstract Mokpo City, located on the southwestern coast of the Korean Peninsula, has been built on one of the largest areas of reclaimed coastal land in Korea. This reclaimed land is currently experiencing significant ground subsidence due to soil consolidation. We have estimated the subsidence rate of Mokpo City (8 × 8 km) using the synthetic aperture radar interferometry (InSAR) and InSAR permanent scatterer (PSInSAR) techniques to analyze 26 JERS-1 SAR images acquired between 1992 and 1998 and six ENVISAT ASAR images acquired in 2004–2005. Mean subsidence velocity, which was clearly related to reclaimed land, was computed from the JERS-1 PSInSAR analysis. The results indicate a continuous and significant subsidence at three sites (Dongmyung, Hadang and Wonsan), where the subsidence velocity has reached more than 5–7 cm/yr in the area of maximum subsidence. The subsidence rate was found to have decreased in Wonsan and Hadang between 1992 and 1998, while it remained steady or increased in Dongmyung during the same period. The subsidence extended to the period of 2004–2005, and the subsidence rate predicted by the JERS-1 PSInSAR analysis using a linear model was confirmed by the ENVISAT ASAR InSAR results. Our results show that InSAR/PSInSAR-based subsidence maps are useful for the long-term monitoring of soil consolidation and for defining risk zones in coastal reclaimed regions.
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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".