PR-686-183908-R01 InSAR Monitoring of Pipeline Geohazards in Vegetated and Very Large Non-Vegetated Areas
Bibliographic record
Abstract
Located on the west side of the Appalachian Mountains, a 14-inch diameter pipeline runs from Kenova to Columbus carrying gasoline and distillates and a 24-inch diameter pipeline runs from Owensboro to Catlettsburg carrying crude products. In 2014, ground movement was identified along the pipeline corridor in an area that was primarily dominated by vegetation. Displacement events similar to this, whether known or unknown have the potential to compromise pipeline integrity. The Appalachian Mountain region is almost entirely classed as high landslide susceptibility by the United States Geological Survey (USGS) and are crossed by numerous transmission pipelines and gathering lines. The Permian Basin located in West Texas is an area of known subtle ground displacement with the potential for larger scale sinkhole development. The area also has extensive multi-operator pipeline networks. Interferometric synthetic aperture radar (InSAR) has successfully been used to monitor ground heave and subsidence due to drilling activity in the Permian Basin. Using InSAR, 3vGeomatics has performed a proof of concept on the effectiveness, reliability, and precision of using L-band SAR satellites for InSAR monitoring of vegetated areas and C-band satellites for monitoring in non-vegetated areas. The InSAR displacement estimates are compared to ground truth data including differential light detection and ranging (LiDAR), in-line-inspection, and ground survey measurements. The goal of this project is to operationalize ongoing InSAR monitoring programs for pipeline networks in vegetated areas and very large non-vegetated areas for operating members. This project has a related webinar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".