New insights into fast ground subsidence in southern Saskatchewan from modeling of RADARSAT-2 DInSAR measurements
Bibliographic record
Abstract
With Radarsat-2 Differential Interferometric Synthetic Aperture Radar (DInSAR) we observed a fast (approximately -10 cm/year) ground subsidence in southern Saskatchewan, affecting some limited areas located between Rice Lake and the city of Saskatoon. The deformation maps were calculated using 2008-2013 RADARSAT-2 SAR data from two different beams: Multi-Looked Fine and Standard. We performed standard InSAR analysis and reconstructed two dimensional, east-west and vertical time series of ground deformation with the Multidimensional Small Baseline subset (MSBAS) method (Samsonov and d'Oreye, 2012). Analysis of the MF3F and S3 time series revealed that the subsidence rate is nearly constant during the entire observation period, which suggests that it is not related to groundwater withdrawal that should have been affected by seasonal variations. We further selected highly coherent ascending and descending interferograms spanning November 2011 - April 2011 for simple elastic modelling. The inversion solves for several parameters, including source depth, precise location and volume change rate. Two regions of subsidence with nearly circular shapes were analyzed. The elastic modelling of the observed deformation is consistent with volume changes of spherical and/or sill-like sources at source depths ranging from 600 to 1500 m. We also investigated the impact of this subsidence on the redistribution of surface water levels and its impact on farming.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".