3D interpretation of geological, 3D seismic and conventional geophysical data from the Darlot Gold Mine
Bibliographic record
Abstract
SummaryThe Darlot gold mine is an Archean orogenic deposit located in the world-class gold and nickel terrain of the Yandal granite-greenstone belt, part of the Yilgarn Craton in Western Australia.A 3D seismic survey centred on the Darlot-Centenary mineralised system was acquired in 2016-2017 with the objective of improving lithological and structural interpretation, and to generally extend understanding of the Darlot 3D mineralised system to support targeting.The capability of modern 3D seismic surveys to image formational contacts and structures in hardrock environments can have a game-changing impact on the effectiveness of brownfields exploration programs because the geometry of mineralised systems can be directly imaged over large volumes of ground. Furthermore, because the formational and structural geometry revealed by 3D seismic also provides the primary control on the physical property variations that magnetic, gravity, and electrical or EM methods respond to, seismic interpretation provides ideal constraints and guidance on the interpretation of other geophysical data. This is particularly valuable when an objective of geophysical interpretation is imaging of second-order controls on physical property variation, such as the effects of alteration.We present the preliminary results of an an integrated geological, petrophysical, seismic and non-seismic geophysical program to effectively support brownfields targeting in hardrock environments.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".