InSAR and the Pipeline Geohazards Toolbox: Instructions for Use As of 2018
Bibliographic record
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is a type of active remote sensing whereby a satellite transmits electromagnetic radiation (microwaves) at the ground and measures the differential phase of the reflected signal over multiple images (or multiple antennas on a single satellite). InSAR has the potential to provide centimeter and even millimeter-scale measurements of displacement over time, but is sensitive to vegetation, topography, and atmospheric effects. We consider herein, the application of InSAR at two known landslides on the Enbridge pipeline system, and discuss the strengths, weaknesses, values, and limitations of its application in the Geohazard Management of landslides impacting pipeline ROW’s. We compare information provided at each site by InSAR (both L-band and X-band) to data derived by mapping using Light Detection and Ranging (LiDAR) or air photographs, to differential LiDAR techniques, and to data derived from subsurface measurements (slope inclinometers). In doing so we find that L-Band data can be an effective tool to establish the extent or footprint of movement (or lack of movement) at known landslide locations, extending the interpretive power of a specialist and the understanding of event magnitude, and potentially affecting the mitigation options. Further, L-Band InSAR can be used in a supporting role to pre-screen areas for active landslides along the right of way (ROW), however, data gaps, a lack of explanatory power, and considerable noise in the results mean that a user step that further considers the terrain, other sources of data, and the identified magnitude, is essential. X-Band InSAR appeared impractical for ROW monitoring where vegetation prevented coherence between images, however, X-Band InSAR was able to detect small displacements at above ground infrastructures.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.677 | 0.648 |
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".