Lithological classification of drill cores using XCT and XRF data: a geometallurgical perspective
Year: 2026
Type: article
Abstract: This study presents an integrated framework for the automated lithological classification of drill cores by combining X-ray computed tomography (XCT) and X-ray fluorescence (XRF) data within a machine learning setting. A total of 293 m of drill cores from the Kalkaroo copper-gold deposit in South Australia were classified into five lithological categories at 5 cm intervals. XCT voxel attenuation values were summarised using percentile-based descriptors to capture density and textural variability, while XRF provided complementary geochemical information. Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers were applied using drill hole-wise leave-one-out cross-validation to mitigate spatial autocorrelation. The integrated XCT-XRF dataset outperformed single-modality inputs, with the XGBoost method achieving the highest predictive performance (Macro-F1 = 0.86). SHapley Additive exPlanations (SHAP) revealed that Cu, Fe, S, Ca and K were the dominant discriminators, while high-end XCT percentiles enhanced the resolution of dense sulphide phases. This proved critical for distinguishing chalcopyrite-rich intervals, which govern copper recovery and concentrate grade, from pyrite-dominant horizons, which increase lime consumption and depress flotation selectivity. Downhole analysis confirmed the geological plausibility of predictions and highlighted transitions with implications for blending, comminution energy and metallurgical forecasting. The results demonstrate that XCT-XRF integration provides a robust and interpretable pathway toward predictive geometallurgy by linking drill core scanning directly to processing outcomes.
Source: Minerals Engineering
Authors: Milena Nasretdinova, Peter Dowd, Chaoshui Xu
Institution: The University of Adelaide
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,220
OpenAlex ID: W7118077426