Seismic properties of the Voisey’s Bay massive sulfide deposit: Insights into approaches to seismic imaging
Bibliographic record
Abstract
ABSTRACT Seismic methods offer significant potential advantages for minerals exploration over more traditional geophysical techniques because of the comparatively high resolution of seismic imaging. This is particularly true as minerals exploration is required to explore deeper to find resources. However, adaptation of seismic imaging techniques to the complex crystalline targets common in the mining environment requires a thorough understanding of the physical properties of the specific combination of ore and host rocks under consideration to choose an appropriate imaging technique. Analysis of the sulfide ores and associated host rocks from the Voisey’s Bay nickel-copper-cobalt deposit indicates that in the pyrrohotite-pentlandite-rich but pyrite-poor assemblage at Voisey’s Bay, seismic velocities are significantly lower (mean∼4500 m/s) than either the felsic or mafic host rocks (mean∼5900 m/s and ∼6400 m/s). This observation is in contrast with pyrite-rich massive sulfide ores that have velocities that are significantly higher than typical host rocks. The large velocity contrast between the Voisey’s Bay ores and their host rocks makes them good targets for tomographic imaging. However, due to the trade-off between the low velocities and high densities of the Voisey’s Bay sulfides, acoustic impedance contrasts can be quite modest making them less attractive for seismic reflection imaging. Detailed analysis of two different mineralized zones at Voisey’s Bay further demonstrated that, depending on the limiting signal-to-noise ratio, the choice of an effective seismic imaging technique is not universal across a mineral deposit and may be affected by subtle variations in sulfide mineralogy and by the structural/magmatic setting. Our analysis clearly indicated that knowledge of physical properties and geologic setting is critical to the choice of which seismic technique to apply in a given exploration setting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".