Application of 3D LSRTM to an onshore walkaway VSP for CO<sub>2</sub> monitoring
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) in vertical seismic profiling (VSP) can be an effective method to monitor time lapse signals on land. We investigate the efficacy of time lapse least squares reverse time migration (LSRTM) applied to a DAS VSP. The goal is to monitor the CO2 plume for a carbon capture and sequestration (CCS) facility while the injection volumes are relatively small. The DAS VSP acquisition was designed as a walkaway VSP with four 2D shot lines arranged in a star pattern centered around the CO2 injection well. The near vertical injection well also serves as the DAS fiber observation well. We use synthetic tests to demonstrate that 3D LSRTM can give interpretable images within a radius of about 400m from the well, despite the predominantly 2D nature of the VSP acquisition. The synthetic tests demonstrate that higher iterations of the LSRTM give improved resolution and amplitude fidelity over a single iteration LSRTM. The tests also help to guide the expectation for the real data results in terms of resolution and amplitude interpretability. The application of LSRTM to real data delineates a credible time-lapse signal within the resolution limitations of the acquisition Presentation Date: Tuesday, October 13, 2020 Session Start Time: 8:30 AM Presentation Time: 9:45 AM Location: 360D Presentation Type: Oral
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".