Fast-Tracking Gold Exploration Below 300m around a mature mine complex – 3D Seismic Case History of the Darlot – Centenary Gold Mine
Bibliographic record
Abstract
The Darlot-Centenary gold deposit is one of the larger known mineralised systems in the southern end of the West Australian Yandal Greenstone Belt, with an estimated 2.7 Moz having been extracted from the Darlot Centenary Mine since 1988. The area is well explored near surface but given the proven endowment there is potential for significant additional mineralisation at depth. With current proven reserves dwindling, Gold Fields recognised the need to identify a technology to fast-track target generation in order to more rapidly evaluate the nearby rock volume.In August 2016 Gold Fields began investigating the potential for 3D reflection seismic to accelerate evaluation of the rock volume accessible via existing workings. In November 2016 a seismic crew was on ground acquiring approximately 150km3 of 3D seismic data (25km2 surface area x 6km depth). The survey coverage was designed to image the local steeply dipping geology and structures. Processing of the seismic dataset was completed in Q1 2017 and Gold Fields has completed preliminary interpretation of the 3D cube.The seismic data has provided a rich 3D picture of the Darlot structural framework to depth, which could not be obtained by any other geophysical method. It has highlighted a number of features with similar characteristics to known mineralisation and has provided a better defined structural framework that has greatly assisted the fundamental geological understanding and further aided ranking of these targets in terms of prospectivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".