InSAR measurement of surface deformations in south Ontario
Bibliographic record
Abstract
Detection of surface deformation can be measured using InSar, a geodetic technique that calculates the interference pattern which results from a difference in phase between images acquired by synthetic aperture radar (SAR). Ground surface deformation, in particular land subsidence is caused by numerous factors, such as: tectonic motion, sediment compaction, thawing permafrost, increased surface loading, Glacial Isostatic Adjustment (GIA), hydro-chemical erosion of karst, decomposition of organic material in soils, mining, anthropogenic fluid withdrawal and surface water/drainage management. Groundwater applications have commonly been completed in areas of extreme subsidence due to dehydration and collapse of fine grained sediment textures; such as in the central valley of California, Nevada, and the Mexico city area. This study examines whether there is potential application of this technique to measure changes in surface elevation in southern Ontario and whether it can be related to changes in groundwater storage. For the southern Ontario study five datasets were used to assess the ground surface deformation and the hydrogeological/hydrologic conditions within the imagery extent. Datasets included: a set of 40 Radarsat-2 images spanning five years, GPS weekly solutions, Real-time kinematic (RTK) gps data, groundwater levels, terrestrial water storage data derived from GRACE satellites and hydrologic data from the Provincial Groundwater Monitoring Network (PGMN). Differential synthetic aperture radar interferometry (D-InSAR) has sub-centimetre precision and high spatial resolution over a large area. To eliminate some of the noise and to reduce geometrical distortions (multilook) images were averaged to a 50 m resolution. A stable reference site RTK TWOO was used a reference point. The TWOO GPS measurements were then added to the InSAR time series deformation maps. Obtaining a coherent signal was difficult and resulted in clustering of signal return from urbanized areas. The roofs and corners of the buildings in urbanized areas can form permanent scatterers, resulting in a more coherent signal. Across the study area, an annual rate of 1 mm to 10 mm of subsidence is observed. The greatest amount of subsidence (> 8 mm/year) is observed along the shore of Lake Ontario. In the absence of a field campaign to support validation the study has concluded that any viable signal attributed to specific geological - hydrogeological controls is within the signal-noise ratio of the study.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".