Assessing the Potential of Passive Seismic Monitoring to Detect a CO2 Leak from the Aquistore Storage Site
Bibliographic record
Abstract
Summary This paper investigates the potential for passive seismic monitoring (PSM) to detect a CO2 leak for two possible scenarios, where a leak is seismic or aseismic. The Aquistore research project in Saskatchewan, Canada provides a suitable site to investigate this because the site is comprehensively monitored. To understand whether induced seismicity would occur following a hypothetical leak, we conduct modelling of fluid flow through a vertical fault. In the event of an aseismic leak, we assess the potential for ambient noise interferometry (ANI) and a tomographic inversion for Rayleigh wave group-velocity maps to provide a suitable CO2 leakage detection tool. A time-lapse ambient noise tomography survey of a CO2 storage site is possible for the first time because data is available preceding and following injection start-up. The fluid-flow modelling shows that a CO2 leak through a vertical fault would result in induced seismic events at depths <500 m. Predicted changes in seismic velocities with CO2 saturation at Aquistore are too small to resolve with the current precision of surface-wave velocity models. However, ANI may be useful to provide a cost-effective early warning system for leakage if uncertainties are reduced.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".