Passive seismic reflection interferometry: A case study from the Aquistore CO<sub>2</sub>storage site, Saskatchewan, Canada
Bibliographic record
Abstract
ABSTRACT Seismic reflection interferometry has recently been tested in a few resource-exploration applications. We have evaluated passive seismic interferometry results for data from the Aquistore CO2 storage site, Saskatchewan, Canada, with the objective of testing the method’s ability to image the subsurface geology and its potential for time-lapse imaging. We analyzed passive seismic data recorded along two perpendicular geophone lines for two time periods that include 23 days in June 2014 and 13 days in February 2015. Beam-forming analysis showed that a nearby power plant is the dominant source of ambient noise. We retrieved virtual shot gathers not only by correlating long noise panels (1 h) for both recording periods, but also by correlating shorter noise panels (10 s) from two days of each recording period. We applied illumination diagnosis to the noise panels from the two chosen days for each period to help suppress the surface waves. Comparisons of the common-midpoints stacked sections, resulting from the virtual shot gathers, with colocated active-source images and log-based synthetic seismograms showed that the best ambient-noise images were obtained for the longest recording periods. The application of illumination diagnosis revealed that only a small percentage of the noise panels are dominated by body waves. Thus, images formed using only this subset of noise panels failed to improve the images obtained from the 23 and 13 days of noise recording. To evaluate the passive images, we performed log-based correlations that showed moderate correlation ranging from approximately 0.5–0.65 in the two-way time range of 0.8–1.5 s. For the 13 to 23 days of noise used in our analysis, the resulting images at the reservoir depth of 3200 m or ∼1.85 s are unlikely to be suitable for time-lapse imaging at this site. This is most likely due to the limited directional illumination and dominance of surface-wave noise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".