Rock Strength Classification in a Brazilian Iron Ore Mine Using Operational Drilling Variables and Machine Learning

Abstract

Rock strength is a key parameter for mine planning and operational optimization, but conventional laboratory testing is costly and provides limited spatial coverage. This study develops a methodology for classifying operational rock-strength classes in a Brazilian iron ore mine using reverse circulation (RC) drilling data and machine learning. Approximately 700 m of drilling data from ten boreholes were analyzed using operational variables acquired by onboard sensors. Data consistency was assessed through twin-hole analysis using Principal Component Analysis (PCA) and LSTM-based autoencoders, while K-Nearest Neighbors (KNN) was used to assess the reproducibility of rule-based operational-state labels. Five supervised algorithms were subsequently evaluated for classifying four operational rock-strength classes assigned by depth correlation with adjacent diamond drillholes and supported by available UCS-based geotechnical records, rather than by direct UCS testing of each RC interval. Random Forest achieved the best internal performance, with a mean accuracy of 90.2% across 50 stratified random partitions, followed by SVM at 88.1%. External validation of Random Forest on an independent blind drillhole achieved 93.1% accuracy, correctly classifying 54 of 58 intervals. These results provide preliminary evidence of model transferability and highlight the potential of RC operational data for indirect rock-strength classification.