Short‐period land 3D multiple attenuation: a case study with interpolation and sparsity
Bibliographic record
Abstract
Exploration and development of the Nisku and Blueridge in West Central Alberta is challenging because these zones are deep, underlie thick coal sequences, and are contaminated with short period multiples. The advent of high resolution sparse Radon Transform multiple attenuation techniques allows us to examine the multiple issue. The Blueridge zone was found to be contaminated by multiples with a variety of small move-outs. The Radon Transform may benefit from well sampled, regular gathers, as input. This can typically only be achieved in land data by borrowing traces from neighboring CMP locations (superbinning) over a significant area. We became concerned that this superbinning might limit the resolution of the transform through a structural smearing effect. We performed 5D interpolation prior to multiple attenuation to eliminate the need for superbinning and reduce the potential effect of smearing on the Radon Transform. Our processing flow resulted in an improved interpretation of the Nisku and Blueridge reservoirs relative to legacy processing flows. A significant improvement was gained from the aggressive AVO compliant noise attenuation and resolution enhancement we applied to all our new products. The interpolation-sparse Radon Transform approach produced superior Tau-p spaces, but improvements appear to be caused by an improvement in signal to noise ratio in the interpolated gathers as well as a reduction in structural smearing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".