Compressive Seismic Imaging: Moving from research to production
Bibliographic record
Abstract
Compressive sensing provides a new framework for sampling signals and wave-fields. This technology, based on non-uniform sampling, sparsity, and optimization can increase sampling efficiency by a factor of two or more in each sampling direction. Seismic data are acquired in four spatial dimensions, so the efficiency gains can approach an order of magnitude. When combined with simultaneous shooting, the gains can be even larger. We refer to our framework for utilizing compressive sensing concepts in seismic acquisition, processing, and imaging as Compressive Seismic Imaging, or CSI. Changing the way we acquire seismic data is a daunting challenge. Any data we acquire must be consistent with safe and efficient field operations, and must be suitable for the geophysical analysis required for exploration and development. Adoption of CSI for production seismic acquisition and processing requires clear demonstrations of not only acquisition efficiency, but also comparable or better results from applications such as imaging, inversion, and 4D analysis. ConocoPhillips utilized a technology qualification process to identify and address risks for CSI deployment, and to facilitate communication with stakeholders. This process was used to define field trials and other actions to assure that CSI not only met efficiency targets, but also satisfied quality requirements. ConocoPhillips has recently completed a full year of production application of CSI to land, marine, and ocean bottom node surveys. In all cases we have achieved significant improvements in both acquisition efficiency and in data quality. Acquisition efficiency improvements achieved in production range from a factor of 4 to as high as 10. In cases where efficiency was used to improve data quality, step function improvements in spatial resolution were achieved as well. The acquired data have also been used for imaging, AVO, and 4D analysis. Direct comparisons of these results have been made to both test data acquired specifically for comparison, and data from adjacent and overlapping surveys. In all cases, quality of the CSI data exceeds that of the comparison data. Presentation Date: Tuesday, September 26, 2017 Start Time: 11:00 AM Location: 371F 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".