Removal of Artefacts in Kx-Ky Domain to Improve Seismic Images over the Brunswick No. 6 Massive Sulphide Deposit, Canada
Bibliographic record
Abstract
Summary Application of seismic method for deep exploration in crystalline rock environment and specifically for mineral exploration has improved dramatically during past two decades. In such environment, prestack dip moveout (DMO) corrections and post stack migration are still important processing tools. Surveys with narrow-azimuth offset distribution often cause artefacts after DMO processing. In this study, we propose a method to reduce those artefacts on final seismic volumes. We illustrate our approach with 3D seismic data acquired over the Brunswick No.6 mine in Canada. Non-orthogonal acquisition geometry and patch of the Brunswick No. 6 3D survey led to narrow-azimuth DMO artefacts that reduced the overall quality of seismic volumes. By using a filtering approach based on the application of weighted Laplacian-Gaussian filter in the Kx-Ky domain, we reduced the noise and improved the continuity of reflections. Better results were obtained by limiting offsets to less than 3 km. We also imaged short and flat reflections observed previously only in the shallow part of prestack time migration (PSTM) volume. Those short reflections appears as diffractions on the filtered DMO stacked section, indicating that they originate from small geological bodies or discontinuities in the subsurface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".