Comparative machine learning analysis for gold mineral prediction using random forest and XGBoost: A data-driven study of the Greater Bendigo Region, Victoria
Why this work is in the frame
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Bibliographic record
Abstract
Gold mineral exploration remains critical to supporting global industries, yet traditional methods relying on manual interpretation of geophysical data are increasingly inefficient and prone to error, particularly when targeting undercover deposits. In Australia, most exploration research has focused on Western Australia, while the Greater Bendigo region in Victoria remains underexplored using modern data-driven approaches, despite its rich mining history and availability of high-resolution geophysical datasets. This study aims to demonstrate that a geospatial analysis methodology based on a machine learning approach enables high-accuracy prediction of gold mineral deposits in Bendigo. The methodology integrates geophysical data, including gravity, total magnetic intensity, and radiometric surveys, combined with geospatial preprocessing, scalable multi-resolution modelling, spatial labelling, and ensemble machine learning techniques, using Random Forest as the primary algorithm and XGBoost as a comparative model. Model performance was assessed using accuracy scores, ROC-AUC metrics, and spatial validation methods, including checkerboard and cluster-based cross-validation, across different spatial scales. Results showed that gravity and magnetic features were the strongest predictors, while radiometric features provided supporting information. Coarser spatial resolutions produced more stable predictions, reflecting regional geological patterns. The study presents a reproducible and adaptable machine learning methodology that addresses key exploration challenges and advances mineral prospectivity analysis using open-access geophysical data. • Machine learning applied to predict gold mineralisation in Greater Bendigo, Victoria. • Integrated gravity, magnetic, and radiometric data with geospatial preprocessing. • Random Forest used as the primary model with XGBoost for comparative analysis. • Model performance evaluated using ROC-AUC through checkerboard and cluster-based validation. • Established a reproducible workflow for spatially informed mineral prospectivity mapping.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it