Study of Subsidence detection by DinSAR and evaluation of some factors to the outcome
Bibliographic record
Abstract
The paper focuses on determining the subsidence by Differential Interferometric Synthetic Aperture Radar (DInSAR) using two methods (Two-pass and Three-pass) and assessing the effect of some error sources to the results. Errors effecting the result include atmospheric, phase due to signal-to-noise ratios, number of looks, pixel misregistration and baseline decorrelation; however, especially significant is (Digital Elevation Model) DEM error. We proved that application of DEM with low precision would create virtual subsidence. The experiment area is Hanoi city, data was Envisat ASAR image, C band with the wavelength of 5.6cm. By using SARscape software for processing, with Two-pass method a virtual subsidence was found at one site in northern part of Hanoi near the Red River (Song Hong). With the Three-pass method this subsidence site did not occurred. Besides, results of surface subsidence detection using DInSAR methods were compared with those measured directly from monitoring stations located close to water supplying wells. As a result, data acquired from Ha Dinh and Thanh Cong stations were closely distributedto those determined by DInSAR method. Unfortunately, the subsidence data acquired by direct measurement had been conducted continuously only from 2000 to 2003 instead to 2005; whereas the Envisat ASAR subsidence data are available only from 2003 to 2004. Therefore, combination of data of direct measurement at Ha Dinh and Thanh Cong stations, and DInSAR processing should provide a linear correlation. . The study proves using DInSAR with Three-pass method would provide more reliable result as compared to the Two-pass method even with a limited amount of images.References Colesanti, C., Ferretti, A., Novali, F., Prati, C. and Rocca, F., 2003: SAR monitoring of progressive and seasonal ground Deformation using the permanent scatterers technique, IEEE Transaction on Geoscience and Remote Sensing, 41, 1685-1701. Dehls, J.F., Basilico, M. and Colesanti, C., 2002: Ground deformation monitoring in the Ranafjord area of Norway by means of the permanent scatterers technique, 23th International Geoscience and Remote Sensing Symposium (Expanded Abstract), Toronto, June 24-28, p. 203-207. Ferretti, A., Prati, C. and Rocca, F., 2000: Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry, IEEE Transaction on Geoscience and Remote Sensing, 38, 2202-2212. Gabriel, A.K., Goldstein, R.M. and Zebker, H. 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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 | 0.000 | 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".