3D seismic imaging of the Lalor volcanogenic massive sulphide deposit, Manitoba, Canada
Bibliographic record
Abstract
ABSTRACT A three‐component three‐dimensional seismic data set was acquired over the Lalor volcanogenic massive sulphide deposit near Snow Lake, Manitoba, Canada, to assess the reflectivity of the ore and further validate the potential of three‐dimensional reflection seismic methods for deep mineral exploration. The Lalor deposit was chosen as a test site as it provided an intact, well‐characterized 25‐Mt‐deep ore deposit with a rich catalogue of geological and geophysical data, as well as extensive drill‐core and drill‐hole geophysical and geological logs. An analysis of physical rock properties from borehole logging data indicates that massive sulphides associated with the zinc‐rich zones could produce prominent reflections, whereas acoustic impedances of zones with disseminated gold do not sufficiently differ from the impedances of the host rocks to produce reflections. The interpretation of the seismic data is constrained with a detailed three‐dimensional lithofacies model constructed from the categorical kriging of 15 lithological units identified in borehole intersections. Processing of the seismic data included prestack dip‐moveout and poststack time migration. Final images reveal some strong reflections associated with the zinc‐rich massive sulphide zones. The most prominent reflection results from the constructive interference of thin and closely spaced massive sulphide zones and felsic–mafic volcanic rock contacts above and below the mineralization. Contacts between felsic and mafic volcanic rocks, including those that were hydrothermally altered and subsequently metamorphosed, produced prominent and continuous reflections that are used to map the main architecture of the footwall rocks. At depth, a series of continuous and conformable reflections indicate the general geometry of the volcanic sequences in the area of the three‐dimensional seismic survey.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".