Passive seismic reflection interferometry: A case study from the Aquistore CO<sub>2</sub>storage site, Saskatchewan, Canada
Bibliographic record
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 [Formula: see text] 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 [Formula: see text] 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 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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".