Improving the Image: 5D Interpolation and COV Gathering of a MegaBin ™ Survey
Bibliographic record
Abstract
Using new processing technologies, it has become possible to improve the images and usefulness of MegaBin ™ 3D seismic surveys shot in areas of limited structure such as the Western Canadian Sedimentary Basin (WCSB). Two technologies are examined here: 5D interpolation and pre-stack time migration (PreSTM) of Common Offset Vectors (COV’s). MegaBin surveys are designed to be interpolated. The new 5D interpolation method allows the interpolation of these surveys pre-stack, preserving azimuthal and offset amplitude variations. Thus it is a natural extension to the MegaBin processing workflow. This technology is tested on a well-shot 3D seismic survey from the WCSB, where the data can be decimated to produce a MegaBin geometry. These decimated data are then interpolated and compared to the original data, both post-stack and pre-stack. COV’s are natural tools that allow the maintenance of azimuth and offset information through migration. They are used on wide-azimuth surveys, such as a MegaBin, in order to retain the azimuth and offset information for later azimuthal analysis, such as seismic fracture detection. The migration of COV’s is tested on this dataset to make certain that this technology, especially when combined with 5D interpolation, produces images that are comparable to those generated by conventional PreSTM.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".