Seismic imaging of the gold deposit and geological structures through reprocessing of legacy seismic profiles near Kloof–Driefontein Complex East Mine, South Africa
Bibliographic record
Abstract
Abstract Two legacy reflection seismic profiles were acquired in 1988, north of the Kloof–Driefontein Complex East Mine in the West Rand goldfield (South Africa), for the purpose of gold exploration and mine planning. These legacy 2D seismic data have been reprocessed using the latest processing tools to improve imaging. Special interest is given to the Black Reef Formation, which hosts a known gold orebody. The original legacy data are of poor quality, especially in areas that are dominated by dolomitic outcrops. To improve the quality of the data, special attention was given to the refraction static correction to enhance the continuity of the reflections below dolomitic rocks. Refraction seismic tomograms from both profiles exhibit three‐layer P‐wave velocity models: (1) topsoil (1000–2000 m/s), (2) a weathered layer ranging from ca. 100 to 300 m in thickness (2000–5000 m/s) and (3) bedrock (> 5000 m/s). Seismic profile OK‐212 shows poor imaging of the Black Reef Formation because of the scattering of seismic energy in the near‐surface due to dolomites from the Transvaal Supergroup, while seismic profile OK‐213 exhibits south‐dipping reflections that are associated with the Black Reef Formation. To improve the structural imaging resolution, we tried pre‐stack time migration, pre‐stack depth migration and post‐stack time migration using the Kirchhoff algorithm. PreSDM most improved the imaging of deeper reflections due to its ability to honour complex lateral variations in the velocity field. Both pre‐stack time migration and post‐stack time migration enhanced the continuity of the near‐surface reflections below the dolomitic rocks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".