Geodetic Monitoring of the Geological Storage of Greenhouse Gas Emissions
Bibliographic record
Abstract
Geodetic monitoring involves the repeated measurement of the deformation of the Earth. As discussed here, it is a cost-effective approach for inferring reservoir integrity and detecting possible leakage associated with the geological storage of greenhouse gas emissions. Most geodetic methods have favorable temporal sampling, from minutes to months depending upon the technique adopted, and can detect anomalous behavior in a timely fashion. Satellite-based approaches such as Interferometric Synthetic Aperture Radar (InSAR), with their high spatial resolution and broad coverage, are particularly well suited for monitoring industrial-scale storage efforts. Multitemporal analysis, such as permanent scatterer techniques, are improving the accuracy of surface displacement measurements to better than 4 – 5 mm. New satellites, including the recent X-band systems, are allowing for the routine estimation of two components of deformation. Data interpretation and inversion techniques may be used to relate the observed displacements to injection-related volume change at depth. InSAR monitoring was used successfully at a gas storage site at In Salah, Algeria, where it was determined that the flow in the reservoir was influenced by large-scale fault/fracture zones. InSAR observations are also key components of the monitoring programs at the Aquistore CO 2 storage project in Canada, and the Illinois Basis Decatur Project in the United States. Current InSAR data from both sites indicate no major surface deformation that might be attributed to the stored carbon dioxide, suggesting that the injected fluid remains at depth.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".