InSAR - Pro-active Remote Sensing for Reservoir Management and Monitoring Environmental Safety
Bibliographic record
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is a satellite-based technology that measures minute changes of surface elevation through time. These deformation changes, often less than 1 mm/month, may be caused by changes in the subsurface (e.g., imbalance between fluid withdrawal and injection, collapse of underground mines), or changes at the ground surface (e.g., surface blisters caused by shallow injection of steam or out-of-zone fluid movement, slope failures). Radar waves from successive passes of polar-orbiting satellites provide trillions of 3m by 3m pixels worldwide on a daily to monthly frequency. Using cloud computing and interferometry, the pixels over areas of interest can be used to monitor activities within oil and gas reservoirs, and also to give warnings of possible problems developing at the surface. Examples are shown for the Belridge giant oil field (California), Groningen giant gas field (the Netherlands), and the Peace River area (Alberta). In the three cases, surface deformation is used to monitor areal conformance in the reservoirs. Also, having satellite passes every 11 days means that reservoirs can be monitored proactively and the resultant datasets have the potential to replace traditional 4D seismic at a cost that is significantly less.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".