Preliminary Monitoring of Ground Slumping Across a Natural Gas Distribution Network With Satellite Radar
Bibliographic record
Abstract
Ground deformation from natural or anthropogenic processes is a significant factor in the integrity management plan for natural gas distribution networks. Rapid or large scale deformation can pose an immediate rupture threat. Smaller, more gradual or repeated ground deformations may lead to material stresses, damage and strain accumulation, posing a longer-term threat. Satellite monitoring can play a key role in pipeline integrity management programs by measuring ground deformation over an entire pipeline network, at high spatial and temporal resolutions with the ability to capture both rapid large scale and subtle, repeated ground movement over a longer period of time. Millimeter accuracy ground deformation estimates are derived from radar satellite imagery using InSAR, a well-established and validated remote sensing technique. InSAR is an effective tool for rapidly identifying new regions requiring ground geotechnical surveys, deriving estimates of deformation rate, extents, and evolution of deformation patterns, for validating or extending traditional ground-based measurements, and for forward-looking operational monitoring. We present the results of InSAR ground deformation monitoring over a natural gas distribution pipeline network in Saskatchewan, Canada. At the case study site, small diameter pipelines (up to 40 years old) have been subjected to ground slumping from a retrogressive landslide affecting multiple lakeshore communities and compounded in recent years by a high water table. Some locations have recently experienced slumping at rates greater than 50 cm/yr leading to important structural issues with roads, buildings, water mains, and gas pipelines. The ground movement analysis is based on RADARSAT-2 satellite imagery acquired at 24-day intervals over a short period in 2015. Thousands of suitable measurement points were identified over two communities on opposite shores of the lake. The measured InSAR deformation time series showed deformation toward the lake. The extents of the deformation are clearly delineated by the InSAR measurements.
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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) | 0.001 | 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".