3D seismic reflection imaging of volcanic-hosted massive sulfide deposits: Insights from reprocessing Halfmile Lake data, New Brunswick, Canada
Bibliographic record
Abstract
Abstract Three-dimensional seismic reflection data from the Halfmile Lake area, New Brunswick, Canada, was reprocessed over an 18- km2 grid to improve the seismic signatures of a 5-million-ton volcanic-hosted massive sulfide (VHMS) deposit located at 1200-m depth, known as the deep zone, as well as key host-rock structures. We chose a prestack dip moveout (DMO) and poststack migration processing sequence to preserve the possible diffraction signature of the deep VHMS zone. Despite the high level of source-generated noise and large statics caused by near-surface conditions, our processing results revealed improved 3D seismic images for shallow and deep structures. Many of the imaged structures were easily correlated with known lithological con-tacts constrained by boreholes and petrophysical measurements. A short, flat-lying segment of high-amplitude reflection at about 800-m depth in the unmigrated cube was interpreted to originate from a small portion of the lower VHMS zone. The DMO stack was characterized by a large, high-amplitude asymmetric diffraction signature originating from the deep VHMS zone. The asymmetry of the diffraction hyperbola relative to the location of the deep zone was interpreted as resulting from a shape effect from the zone, with the strongest amplitudes along the diffraction hyperbola found north-northwest of the apex. This indicated that the deep VHMS zone dips in a similar direction. This diagnostic diffraction signature was not preserved with the prestack migration approach previously implemented for processing Halfmile Lake data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".