Pros and Cons of 2D Crooked Seismic Profiles for Deep Mineral Exploration - A Comparison with 3D Surveys in Geologically Complex Mining Environment
Bibliographic record
Abstract
Summary Despite being applied for nearly three decades now, the seismic methods for deep mineral exploration are routinely carried out using 2D profiles along existing roads requiring crooked-line methods. While the assumption of a 2D geology is rarely valid in most mining environments, no follow up or very little 3D surveys are attempted. Using synthetic seismic data and case studies, we illustrate that 3D seismic surveys should ultimately be carried out for detailed interpretations and for direct targeting of mineralization. We show for example that a bright-spot seismic anomaly observed on a 2D seismic profile was associated with an approximately 6 Mt of massive sulphide mineralization that was targeted, after being delineated on 3D seismic volume, about 500–700 m off the 2D profile at about 1.2 km depth but shallower than that observed in the 2D profile. The mineralization produced a noticeable diffraction signal in the 3D unmigrated volume with certain characteristics providing information about the geometry and possibly the mineralization content. Using another case study we show how sometimes 2D crooked-line data can provide information about accurate delineation of small objects in 3D. Nevertheless we argue that nothing would replace a proper 3D seismic survey and encourage this to be done if exploration to be successful.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".