Reprocessing legacy three-dimensional seismic data from the Halfmile Lake and Brunswick No. 6 volcanogenic massive sulphide deposits, New Brunswick, Canada
Bibliographic record
Abstract
We reprocessed legacy three-dimensional (3D) seismic data from the Halfmile Lake and Brunswick areas, both of which were acquired for mineral exploration in the Bathurst Mining Camp, New Brunswick. Each 3D seismic survey was acquired over known volcanogenic massive sulphide deposits and covered areas with strong mineral potential. Most improvements resulted from a reduction of coherent and random noise on prestack gathers and from an improved velocity model, combined with re-imaging with dip moveout corrections and poststack migration or prestack time migration. At Halfmile Lake, the new imaging results show the Deep zone and a possible extension of the sulphide mineralization at greater depth. True amplitude processing has shown that this anomaly has strong amplitudes and is offset from the Deep zone by a shallowly dipping fault (<15°). With the clearer geological context provided by our results, this anomaly, which appears as a stand-alone anomaly on an original image obtained by Noranda Exploration Ltd., becomes a defendable exploration target. Nonorthogonal acquisition geometry and receiver patches of the Brunswick No. 6 3D seismic survey generated artefacts after dip moveout processing that reduced the overall quality of the seismic volumes. By using a filtering approach based on the application of a weighted Laplacian-Gaussian filter in the Kx–Ky domain, we reduced the noise and improved the continuity of reflections. We also imaged the short and flat reflections observed previously only in the shallow part of prestack time migrated data. These short reflections appear as diffractions on the filtered stacked section with dip moveout corrections, indicating that they originate from small geological bodies or discontinuities in the subsurface.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".