Reprocessing of legacy seismic data for gold exploration: case study from Witwatersrand goldfields, South Africa
Bibliographic record
Abstract
Summary Legacy data are defined as previously acquired data that are no longer in use. Their restoration requires substantial time and money, without the promise of yielding rewarding results. These legacy data sets are often accompanied by poorly preserved documentation, outdated coordinates, and are stored on old tech (e.g., tapes or printed versions/hard copies) that make the data hard to use. The new information acquired from the legacy data may profit future mine planning operations by finding new ore deposits, giving a superior estimation of the resources and information that will assist with sitting and sinking future shafts. In this study we present results from the reprocessed legacy seismic data from Witwatersrand goldfields (South Africa). The purpose of the study is to improve the imaging of the gold orebody known as the Ventersdorp Contact Reef (VCR), which is mined at the Kloof Gold Mine. The VCR occurs at an interface between the Ventersdorp Supergroup and Central Rand Group with contrasting densities and seismic velocities, which makes it a good target for the seismic methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".