Application of automated detection techniques in magnetic data for identification of Cu-Au porphyries
Bibliographic record
Abstract
SummaryAutomated shape recognition technology has been developed for application to porphyry exploration, through a joint research initiative between Barrick Gold and the Centre of Exploration Targeting at the University of Western Australia, commencing in 2009. The result is the Development of the Porphyry Texture Filter for application to magnetic datasets.Many mineralised porphyries display concentric zonation in their magnetic character as a by-product of extensive hydrothermal alteration systems and secondary magnetite development/destruction. This characteristic magnetic signature can be exploited by image processing techniques enabling the enhancement, identification and quantification of features. Features must agree with a user-defined set of criteria for size, shape and magnetic contrast.Development of the technique was carried out on the world class Reko Diq porphyry system resulting in successful identification of all major known mineralised porphyry centres and additional targets within the camp. User control over filter parameters has resulted in the successful application of the filter on projects in a range of geological and erosional environments.The ability to rapidly characterise porphyry-like signatures using mathematical principles and geometries results in an unbiased geophysical target layer. When integrated with other geoscientific data, the filter has consistently supported target generation activities.Examples of the Porphyry Texture Filter application and results from Reko Diq, Grasberg and active exploration projects are shown.
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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 | 0.000 |
| Research integrity | 0.000 | 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".