Enhancing 3D post‐stack seismic data acquired in hardrock environment using 2D curvelet transform
Bibliographic record
Abstract
ABSTRACT Seismic data acquired in hardrock environment pose a special challenge for processing. Frequent lack of clear coherent events hinders imaging and interpretation. Additional difficulty arises from the presence of significant amount of cultural noise associated with production and processing of ore, which often remains in the processed, stacked data. Motivated by those challenges, we developed an efficient workflow of denoising 3D post‐stack seismic data by using 2D discrete curvelet transform aimed at improving signal‐to‐noise ratio of the data. Our approach is based on the adjustment of the thresholds according to scales and angles in the curvelet domain, making parameterization flexible. We demonstrate effectiveness of our method using 3D post‐stack volumes from the three different mining camps in Canada, which were characterized by variable data quality. Remarkable signal enhancement, confirmed by the improvements in the mean signal‐to‐noise ratio of the dataset, is obtained not only due to random energy attenuation but also by removal of certain features corrupting the data (e.g., acquisition footprint). Comparison with the F‐X/F‐XY deconvolution results shows the superiority of our algorithm in respect to signal enhancement, signal preservation, and amount of the removed noise. Imaged structures, even if initially dominated by random energy, are easier to follow after curvelet denoising and enhanced for interpretation. Therefore, our approach can significantly reduce interpretation uncertainties when dealing with the seismic data acquired in the hardrock environment.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".