Time-lapse processing and imaging of the Snowflake 3D DAS VSP CO2 monitoring dataset
Bibliographic record
Abstract
Distributed Acoustic Sensing (DAS) data acquired in a Vertical Seismic Profile (VSP) configuration is being actively considered as a candidate low-cost monitoring technology for CO2 injection and storage. The University of Calgary “Snowflake” 3D VSP experiment, currently with two surveys spanning 2018-2022, was carried out in part to aid in this assessment. Optimizing 3D time-lapse VSP-DAS data processing and imaging involves several open questions, some of which we address in this study. Our primary focus is on enhancing the quality of upgoing wave data through a comprehensive approach, including phase analysis, denoising, separation of upgoing and downgoing waves, and wavelet characterization. Additionally, to further enhance imaging results, we employ both azimuthally-dependent and reflection angle-dependent reverse time migration (RTM) methodologies. The final imaging results arising from this approach lead to VSP-DAS time-lapse imaging, which may not only be relevant in the CO2 monitoring problem but in a range of applications of this technology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".