EcoSeis: A novel acquisition method for optimizing seismic resolution while minimizing environmental footprint
Bibliographic record
Abstract
Abstract All exploration and production projects, whether for oil-and-gas, mining, or clean-technology applications, begin with an accurate image of the subsurface. Many technologies have been developed to enable the acquisition of cost-effective seismic data, with high-density land seismic programs becoming commonplace. However, as the industry progresses and the long-term surface footprint associated with these programs becomes better understood, new methods are needed to reduce the environmental impact of seismic data acquisition while maintaining sufficient subsurface resolution for accurate resource development. New acquisition geometries are typically easier to create than test in the field due to the high cost of field acquisition and processing. However, by using existing data acquired in a grid, one can decimate the original data set into multiple geometries and process them. This provides an opportunity to fully test new geometries without the expense of field acquisition. In this paper, we present processing, interpretation, and inversion tests from an existing ultra-high-density oil-sands seismic data set decimated based on ecologically improved program designs. We then measure and compare the results to understand the impact of these geometries on subsurface resolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".