Automated Hyperspectral Ore–Waste Discrimination for a Gold Mine: Comparative Study of Data-Driven and Knowledge-Based Approaches in Laboratory and Field Environments
Bibliographic record
Abstract
Hyperspectral imaging has been increasingly used in mining for detailed mineral characterization and enhanced ore–waste discrimination, which is essential for optimizing resource extraction. However, the full deployment of this technology still faces challenges due to the variability of field conditions and the spectral complexity inherent in real-world mining environments. In this study, we compare the performance of two approaches for ore–waste discrimination in both laboratory and actual mine site conditions: (i) a data-driven feature extraction (FE) method and (ii) a knowledge-based mineral mapping method. Rock samples, including ore and waste from an open-pit gold mine, were obtained and scanned using a hyperspectral imaging system under laboratory conditions. The FE method, which quantifies the frequency absorption peaks at different wavelengths for a given rock sample, was used to train three discriminative models using the random forest classifier (RFC), support vector classification (SVC), and K-nearest neighbor classifier (KNNC) algorithms, with RFC achieving the highest performance with an F1-score of 0.95 for the laboratory data. The mineral mapping method, which quantifies the presence of pyrite, calcite, and potassium feldspar based on prior geochemical analysis, yielded an F1-score of 0.78 for the ore class using the RFC algorithm. In the next step, the performance of the developed discriminative models was tested using hyperspectral data of two muck piles scanned in the open-pit gold mine. The results demonstrated the robustness of the mineral mapping method under field conditions compared to the FE method. These results highlight hyperspectral imaging as a valuable tool for improving ore-sorting efficiency in mining operations.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".