Microseismic event analysis using multi-technology sensors at the Quest CCS site
Bibliographic record
Abstract
Microseismic monitoring plays a crucial role in assessing the effectiveness and integrity of Carbon Capture and Storage (CCS) projects. By the detection of microearthquakes we can gain real-time insights into the pressure and stress perturbation due to injection operations, aiding in the detection of potential leakage and ensuring the long-term viability of carbon sequestration efforts.At the Quest CCS site in Alberta, Canada, CO2 injection into a 2 km depth saline reservoir is ongoing since 2015 at a rate of one million tonnes per year. Several hundreds of small-magnitude seismic events have been located in the Precambrian basement below the reservoir. A spatio-temporal analysis of seismicity reveals clustered as well as more diffuse distributions of events. At the Quest site various microseismic monitoring technologies are in place including a downhole 8-level 3-component geophone string, temporary surface nodes arranged in mini-arrays, and downhole optical distributed acoustic sensing (DAS) fiber. The site offers an ideal opportunity to compare and combine the different setups with respect to event detection thresholds and location uncertainties. We demonstrate the importance of advanced signal and array processing techniques and highlight the advantages and disadvantages of different sensor technologies.
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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 | 0.001 |
| 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 | 0.000 | 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".