Monitoring CO<sub>2</sub> Injection at the CaMI Field Research Station Using Microseismic Noise Sources
Bibliographic record
Abstract
Abstract Monitoring subsurface velocity variations due to industrial activities using passive seismic imaging techniques has gained popularity in recent years. In this study, we examine the spatiotemporal variations of persistent, non‐ambient seismic noise during CO2 sequestration at the Containment and Monitoring Institute Field Research Station near Brooks, Alberta, Canada. Based on the temporal migration and power spectral density (PSD) analysis of continuous seismic records from both a dense geophone array and “X”‐shaped geophone lines operated from June to August 2019, we detect two non‐ambient local noise sources correlated with the local industrial activities during the two months: a dominant noise source (1–5 Hz) southeast of the study region and a slightly weaker noise source in the higher frequency range (5–15 Hz) around the injection well. The former noise source overlaps with the operations near a submersible disposal pump. The substantial diurnal variations in noise levels in PSD as a function of time of day, month and location are further evidence of these two noise sources. We propose that the persistent noise source around the injection well originated from the subsurface microtremors caused by the coupled interaction between the injected CO2 fluid and formation rocks. The proposed methods based on passive microseismic noise offer a potentially valuable strategy for long‐term evaluation of the safety of CO2 sequestration, which can be extended to future integrity monitoring of underground energy storage.
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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 | 0.000 | 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) | 0.001 | 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".