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 CO 2 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 CO 2 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 CO 2 sequestration, which can be extended to future integrity monitoring of underground energy storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".