Seismic super resolution method for enhancing stratigraphic interpretation
Bibliographic record
Abstract
Structural and stratigraphic interpretations are often limited by seismic resolution. Reservoir characterization is also highly dependent on the quality and resolution of the seismic. Here we describe a post-imaging inversion method for broadening the seismic bandwidth. The method was inspired by super resolution techniques from computer vision. By imposing sparsity/coherency assumptions on geophysical parameters, we formulate a constrained minimization problem to invert high-resolution seismic from its low-resolution counterpart. The method uses seismic only to drive the inversion which avoids introducing bias from well data. We demonstrate the method using a simple synthetic dataset and then discuss its first real data application at Bohai A field. Comparing to the input seismic, seismic super resolution was able to extend the data bandwidth for both low and high ends. The inverted data revealed sand connectivity and stacking pattern at the reservoir level which was not observed on the original seismic. After examining against well data, the increased resolution was proven useful for stratigraphic interpretation and well planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".