3D imaging challenges in steeply dipping mining structures: New lights on acquisition geometry and processing from the Brunswick no. 6 seismic data, Canada
Bibliographic record
Abstract
ABSTRACT We have analyzed and processed a 38-km2 nonorthogonal 3D surface reflection seismic data in the Brunswick no. 6 area to better understand the effect of acquisition geometry on the resultant image and to provide 3D information about the main geologic structures hosting the mineralization. The 3D data were processed using a conventional prestack dip moveout (DMO) and poststack migration algorithm with special focus on refraction static corrections, velocity analysis, and DMO corrections that are important for the data recorded in crystalline environment. However, the nonorthogonal nature of the 3D data combined with its narrow-azimuth, irregular offset distributions, and 2D nature of midpoint distribution in common depth point bins resulted in a lower quality seismic image than those observed on a series of 2D seismic profiles collected in the area prior to the 3D data acquisition. 2D wavenumber spectrum of the data suggests acquisition footprint associated with the data. Most of the noise associated with the acquisition footprint manifested itself as short-length, high-amplitude shallow reflections but largely were attenuated using a dip filter running in the wavenumber domain. Various bin size and geometries were tested, and the best result was obtained using rectangular bins aligned in the orientation of the shot lines. The processing results indicated that the highly prospective and mineralized Brunswick horizon is part of a continuous reflective package that could guide future deep mineral exploration in this mining camp.
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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 | 0.001 |
| 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".