Seismic methods in mineral exploration and mine planning — Introduction
Bibliographic record
Abstract
Across the globe, the mineral industry is seeking technology to improve exploration efficiency at depth and to help design safer and more productive mines. Seismic methods are increasingly used by this industry for a wide range of commodities including base metals, uranium, diamonds, and precious metals. Seismic methods provide high-resolution images of geologic structures hosting mineral deposits and, in a few cases, can be used for direct targeting of mineral deposits. Applications are not limited to only surface seismic surveys, but also include borehole seismic methods such as VSP and crosshole imaging. To date, tens of 2D and 3D surface seismic surveys have been acquired in Canada, Europe, Australia, and South Africa (see Malehmir et al., 2012) to help in targeting mineral deposits at depth or for designing deep mines. The steadily increasing usage of reflection seismic methods demonstrates that they are finally becoming recognized and established within the mining sector. This brings new opportunities for geophysicists, but also new challenges. Some of these challenges and opportunities are presented and discussed in the special section. This special issue contains a wide range of topics, from petrophysical studies to data acquisition, processing and imaging, as well as 2D and 3D seismic modeling of mineral deposits and their host-rock structures. Papers from both industry and academia are presented, which illustrate the importance of seismic methods not only in the hydrocarbon industry, but also in the mineral industry. Malehmir et al. review important contributions that have been made in developing seismic techniques for the mining industry with focus on four main regions: Australia, Europe, Canada, and South …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".