Seismic methods in mineral exploration and mine planning: A general overview of past and present case histories and a look into the future
Bibliographic record
Abstract
ABSTRACT Due to high metal prices and increased difficulties in finding shallower deposits, the exploration for and exploitation of mineral resources is expected to move to greater depths. Consequently, seismic methods will become a more important tool to help unravel structures hosting mineral deposits at great depth for mine planning and exploration. These methods also can be used with varying degrees of success to directly target mineral deposits at depth. We review important contributions that have been made in developing these techniques for the mining industry with focus on four main regions: Australia, Europe, Canada, and South Africa. A wide range of case studies are covered, including some that are published in the special issue accompanying this article, from surface to borehole seismic methods, as well as petrophysical data and seismic modeling of mineral deposits. At present, high-resolution 2D surveys mostly are performed in mining areas, but there is a general increasing trend in the use of 3D seismic methods, especially in mature mining camps.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".