Targeted nullspace shuttles as enablers of time-lapse CO2 monitoring with sparse acquisition
Bibliographic record
Abstract
Time-lapse seismic monitoring, when based on full waveform inversion methodologies, relies on both repeatability of baseline, and monitor survey geometries, and complete acquisition (i.e., dense sampling, and wide offsets and azimuths). The costs arising from this make it impractical for use in long-term CO2 monitoring programs. Here we address this issue by examining targeted nullspace shuttles, which are FWI model updates designed to be applied post-convergence and to explore the space of models which conserve data misfit. If, as we assume, time-lapse model artifacts and 4D noise caused by sparse acquisition are not required to maintain data misfit, it follows that specially designed time-lapse nullspace shuttles might exist which suppress artifacts due to these low-cost acquisition geometries, opening them up to practical use. To test this idea, we set up numerical experiments in which we invert synthetic VSP data designed to mimic a time-lapse survey at the Carbon Management Canada Newell County Facility in Alberta, Canada. We vary the baseline and monitoring acquisition geometries, and compute shuttles designed to minimize model differences and maintain misfit with these varying datasets. Our observation is that, if the baseline acquisition is sufficiently complete, an extremely limited number of monitoring sources and sensors are needed to identify actual subsurface change and eliminate a large fraction of the acquisition artifacts.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".