The Performance of an ACROSS Permanent Seismic Source for Time Lapse Seismic at the Aquistore CO2 Storage Site
Bibliographic record
Abstract
Abstract Repeatability is one of the most important factors for time-lapse seismic surveys. Several types of permanent seismic sources have been developed previously to improve repeatability but monitoring of deep targets is still challenging because of the small power of such sources. We operated an ACROSS (Accurately Controlled, Routinely Operated Signal System) permanent seismic source at the Aquistore CO2 storage field in Saskatchewan, Canada. This source is known to excite continuous highly repeatable seismic signals by rotating an eccentric mass and to generate large force that is comparable to Vibroseis. This study analysed the quality and repeatability of ACROSS data (before massive CO2 injection) acquired in 4 periods (December 2014, and March, June, and October of 2015). Target reflections from 3300m depth can be observed on the ACROSS shot gather. After stacking over several hours, the S/N ratio of a raw shot gather from ACROSS is found to be comparable with a Vibroseis gather. Although low frequency and high amplitude noise is not negligible at near offset, the quality and repeatability of far offset data are excellent as the time shift calculated by cross correlation between different data is basically less than 1 milisecond. Data in March show a few milisecond time shift on all traces which is presumably caused by near surface changes locally around ACROSS but a global matching filter successfully corrected the time shifts and reduces its median to 0.74 ms which is less than expected time shifts by a previous study. This study indicates the high potential of an ACROSS survey to detect small changes for deep targets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".