Time-Lapse Monitoring Using a Permanent Source ACROSS and Surface-Buried Geophones at the Aquistore CO2 Storage Site.
Bibliographic record
Abstract
Summary We demonstrate the effectiveness of reservoir monitoring using a permanent seismic source (accurately controlled, routinely operated signal system: ACROSS), and a sparse areal permanent array of buried geophones at the Aquistore CO₂ storage site. Since 2016, monitoring surveys have been conducted 4 times. Before real data analysis, a modeling study using a single source and a sparse array that matched the actual field geometry shows that our monitoring system can detect physical response changes despite the areal imaging distortion. By applying time-lapse processing and analysis to the field data, high NRMS amplitude anomalies are detected at the reservoir depth. The location and lateral growth pattern of the anomalies associated with CO₂ injection are consistent with the reported results of the 3D seismic data, even though the location is shifted to the northeast direction, which is outward from the source location. The anomalies may be a sign of seismic response changes associated with CO₂ injection, although it should be noted that they tend to be located outward from the source location as observed in the modeling study. Even though this monitoring system cannot bring comparable results to 3D seismic surveys, there is a way to use it for low-cost surveillance.
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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 | 0.000 | 0.000 |
| 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".