The Value of Seismics in Mineral Exploration and Mine Safety
Bibliographic record
Abstract
Summary The word “seismics” in the geoscience community is often used synonymously with “oil and gas”, despite its successes in other applications, for example, in mineral exploration, engineering application, mine planning and safety.Over the past few decades, the method has been developed and successfully used for mineral exploration, mine planning, and safety in “hard rock” metallogenic provinces worldwide (e.g., Australia, Europe, Canada, and South Africa), leading to the discovery of giant minerals and metal deposits.However, despite these successes, the method's capabilities in mining still remains less-known to many geoscientists and some mining companies are still reluctant to use it for “hard rock” exploration and mining.The purpose of this paper is to demonstrate how the reflection seismic method has been successfully used to explore and discover some of the world's largest mineral and metal deposits that are located deep underground - where exploration drilling is more costly and risky. A wide range of case studies from hard rock environments are covered, for example, from South Africa and Canada.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".