Petrophysics and Exploration Targeting: The Value Proposition
Bibliographic record
Abstract
There is still much that needs to be understood about the physical properties of rocks in mineralised geological environments. This knowledge gap becomes more important as the transition to deeper exploration targets under cover occurs, with an associated greater reliance on geophysical exploration methods. The major challenge associated with understanding petrophysical data is not making the measurement, but rather understanding the results. The interpretation of the data is a cross disciplinary problem. Fundamentally it is necessary to understand the rock mineralogy and geochemistry to put the petrophysics in context with the geophysical results. Several case studies are presented where the petrophysics have determined not only which geophysical techniques to apply but whether a geophysical target has indeed been tested.Drill testing EM plate approximations for nickel sulphide and volcanogenic massive suphide (VHMS) ore deposits can benefit from inductive conductivity measurements on core as it can determine whether an EM conductor has been intersected. Chargeability highs associated with porphyry copper mineralisation is indicative of disseminated pyrite in the propylitic and pyrite +/- chalcopyrite +/-bornite in the potassic alteration zones and higher chargeability does not necessarily mean more copper. In most porphyry systems magnetite is coarse-grained, therefore a world class porphyry deposit should not have dominant remanent effects and the only likely source of remanence features in younger terrains are oxidised mafic intrusions and skarns. Furthermore, porosity and specific types of alteration (argillization) display the strongest correlations with resistivity and can be tied to gold distribution in Carlin Type Deposits.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".