The Utility of Machine Learning in Identification of Key Geophysical and Geochemical Datasets: A Case Study in Lithological Mapping in the Central African Copper Belt
Bibliographic record
Abstract
Random Forests, a supervised machine learning algorithm, provides a robust, data driven means of predicting lithology from geophysical, geochemical and remote sensing data. As an essential part of input selection, datasets are ranked in order of importance to the classification outcome. Those ranked most important provide, on average, the most decisive split between lithological classes. These rankings provide explorers with an additional line of reasoning to complement conventional, geophysical and geochemical interpretation workflows. The approach shows potential to aid in identifying important criteria for distinguishing geological map units during early stage exploration. This can assist in directing subsequent expenditure towards the acquisition and further development of datasets which will be the most productive for mapping.In this case study, we use Random Forests to classify the lithology of a project in the Central African Copper-Belt, Zambia. The project area boasts extensive magnetic, radiometric, electromagnetic and multi-element geochemical coverage but only sparse geological observations. Under various training data paradigms, Random Forests produced a series of varying but closely related lithological maps. In this study, training data were restricted to outcrop, simulating the data available at the early stages of the project. Variable ranking highlighted those datasets which were of greatest importance to the result. Both geophysical and geochemical datasets were well represented in the highest ranking variables, reinforcing the importance of access to both data types. Further analysis showed that in many cases, the importance of high ranking datasets had a plausible geological explanation, often consistent with conventional interpretation. In other cases the method provides new insights, identifying datasets which may not have been considered from the outset of a new project.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".