Passive seismic monitoring of carbon dioxide storage at Weyburn
Bibliographic record
Abstract
Carbon capture and storage (CCS) is currently one of several candidate technologies for reducing the emission of industrial CO2 to the atmosphere. As plans for large-scale geological storage of CO2 are being considered, it is clear that monitoring programs will be required to demonstrate security of the CO2 within the storage complex. Numerous geophysical monitoring techniques are currently being tested for this purpose, including controlled-source time-lapse reflection seismology, satellite synthetic aperture radar interferometry, electromagnetic sounding, gravity, and others. Passive seismic monitoring is an additional technique under consideration that complements these other techniques, and has potential as a cost-effective method of demonstrating storage security. This is particularly true over longer periods of time, as passive seismic arrays cost relatively little to maintain. Of the large-scale CCS pilot projects currently operational, thus far only the IEA GHG Weyburn-Midale CO2 Monitoring and Storage Project has included passive seismic monitoring. Here we present the results from five years of passive seismic monitoring at Weyburn, and discuss the lessons learnt that can be applied when deploying passive seismics to monitor future CCS operations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".