Onshore Sparse Seismic Monitoring Design Scenario using Permanent Sources and Receivers
Bibliographic record
Abstract
Summary Sparse seismic monitoring of Geological CO2 Storage (GCS) with highly repeatable acquisitions from permanent sources and receivers can provide a cost-effective approach to Measurement, Monitoring and Verification (MMV), either by replacing or reducing conventional time-lapse 2D and 3D surface seismic acquisitions. The cost of installing and maintaining permanent monitoring equipment are not negligible, and the practicality of monitoring a large-scale CO2 plume over decades of injection remains a daunting challenge. We present a conceptual exercise exploring a sparse monitoring scenario for a project analogous to Quest in Alberta, with the goal of determining what permanent monitoring infrastructure would be required to adequately monitor a CO2 plume over a period of 30 years or more. This scenario involved six semi-permanent, re-deployable seismic sources, eleven permanent receiver arrays, and up to twenty permanent source locations. The coarse spatial sampling is adequate for detecting and delineating a CO2 plume over 30 years while also providing post-closure monitoring capability. Whether to reduce the cost of conventional seismic monitoring for GCS, or for deployment in areas where conventional surface acquisition is impractical or prohibited, sparse seismic monitoring is a promising addition to the GCS MMV toolbox.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".