Enhancing base-metal exploration with seismic imagingThis article is one of a series of papers published in this Special Issue on the theme<i>Lithoprobe — parameters, processes, and the evolution of a continent</i>.
Bibliographic record
Abstract
Commencing in 1988 and continuing for 5 years, Lithoprobe acquired a series of high-resolution seismic experiments within and near base-metal mining camps in Canada, including the Abitibi subprovince of Quebec and Ontario, the world-class Sudbury Ni–Cu mining district, the Buchans mine in Newfoundland, and the Thompson Ni belt in Manitoba. This work, undertaken in close cooperation with the Geological Survey of Canada and major Canadian mining companies, stimulated an intensive and broadened series of followup studies with the common objective of assessing potential applications of multichannel seismic (MCS) imaging for deep mineral exploration and mine development. This research was motivated by a widely recognized disparity between the depths from which ores can be profitably mined (up to 2 km or more) and the resolving depths (typically <500 m) of commonly used geophysical methods for mineral exploration. Initial rock-property studies established that the expected contrast in acoustic impedance between ores and host rocks should be sufficient to generate observable reflections and (or) scattered waves. For an ore deposit to be directly detectable with MCS, however, it is also necessary for it to meet geometrical criteria including a minimum thickness of 1/8 wavelenth (typically ∼5 m) and a lateral extent similar to the Fresnel radius (typically ∼100 m). Both Lithoprobe and followup seismic studies, calibrated with borehole data, reveal that lithologic contacts that are characterized by large impedance contrast and significant lateral continuity, such as igneous intrusive contacts between mafic and felsic rocks, are the most likely features to be imaged with the MCS techniques. In some camps such as Buchans, however, faults and shear zones are better imaged than lithologic contacts. In either case, these studies show that well-designed and carefully processed seismic profiles can provide a valuable geophysical tool for interpreting the stratigraphic and structural framework of mineral systems and, more rarely, direct-detection capabilities for deep ore deposits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".