Seismic monitoring using compact phased arrays: CO2 sequestration monitoring
Bibliographic record
Abstract
A network of passive, persistent, permanent, compact volumetric phased arrays (e.g. SADAR® arrays, Nyffenegger et al., 2022) features multiple technical advantages for monitoring seismic activity at and around CO2 injection and storage sites compared to a traditional surface network or downhole sensor strings at the depth of a reservoir. A limited demonstration network was installed at Carbon Management Canada’s (CMC) Containment and Monitoring Institute (CaMI) Field Research Station, and analysis of the acquired data has produced an event bulletin covering ∼80 days. Coherent processing (i.e. beamforming) of the acquired data increases the SNR allowing detection and localization of 406 subsurface microseismic events down to moment magnitude Mw = -3 within the network geographical extent. Analyst vetting of these events indicates that the signals typically exhibit clear onset of P waves in the optimal beam, enabling arrival time picking with a lower uncertainty than for the shear waves that dominate the records observed by other surface networks. In addition, using the optimal beam attributes associated to the event phase arrivals allows identifying signals arriving from below and exterior to the network’s coverage (e.g., episodic low-frequency tremor), potentially associated with other natural or man-made seismicity also worth monitoring.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".