Linking Satellite InSAR Ground Deformation Data Into Operational Decision-Making
Bibliographic record
Abstract
Abstract Mature pipeline geohazard management programs assess information related to ground movement hazards, such as landslides or subsidence, in conjunction with the condition of the pipeline (vulnerability). This enables the development of site-specific risk rankings and intervention strategies along extensive networks of pipeline infrastructure. As technology and software tools have evolved, pipeline operators are better able to leverage spatial data from a variety of ground observations and remote-sensing data for an improved understanding of the hazard. Remote sensing data can be used to identify potential geohazard features, monitor known geohazard sites, and assess the rate of ground displacement. When combined with geotechnical subject matter expert (SME) knowledge of ground conditions, gained through desktop assessments, field inspections, and analysis of in-line inspection tool data for example, remote sensing data can inform the prioritization of next actions. In recent years, there has been a notable increase in the adoption of interferometric synthetic aperture radar (InSAR) data to support geohazard identification and monitoring. Consequently, numerous pipeline operators are actively seeking enhanced methodologies to leverage this data for risk-informed decision-making. This paper provides a comprehensive overview of InSAR considerations and limitations and explores how the data can be integrated into geohazard management programs. We also delve into the potential to correlate InSAR observations with site-specific risk classifications to determine the timing of operational actions and possible interventions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".