Predicting rock hardness using Gaussian weighted moving average filter on borehole data and machine learning

• Published in: Minerals engineering 2023-12, Vol.204, p.108448, Article 108448
• Main Authors: Houshmand, Negin, Esmaeili, Kamran, Goodfellow, Sebastian, Carlos Ordóñez-Calderón, Juan
• Format: Article
• Language: English
• Subjects: Leeb hardness; Machine learning; P- and S-wave velocity; Portable XRF; Rock hardness prediction; Weighted moving average
• ISSN: 0892-6875
• DOI: 10.1016/j.mineng.2023.108448

•The weighted moving average technique was introduced for smoothing local features.•Unsupervised learning was used for data interpretation and visualization.•More than 500 m of core samples were logged using a multi-sensor core logging.•Machine learning models were utilized for rock hardness prediction. A comprehensive understanding of the hardness of ore being handled and processed in a mining operation can significantly improve operational efficiencies. This is feasible by providing valuable data to support decision-making through the mining value chain (drilling, blasting, loading, comminution). This study presents the results of a machine learning (ML) approach for rock hardness prediction using rock’s geophysical and geochemical features. Core samples from several mine sites were logged using a multi-sensor core logging (MSCL) system. Measurements include ultrasonic P- and S-wave velocity, elemental concentration via portable X-Ray fluorescence analyzers (pXRF), and Leeb rebound hardness, measured every 30 cm along 564 m of core samples. K-Means and PCA were used for better interpretation of the data. Supervised ML models (XGBoost and Random Forest) were utilized to predict rock hardness using the elemental concentrations and ultrasonic velocities as predictors. Since the data was collected automatically with predefined intervals, some of the measurement points were near fractures or veins. The Gaussian weighted moving average (WMA) was used to smooth out variations in geochemistry or hardness caused by local features that do not reflect the overall rock characteristics. This approach is effective for building ML models to become less susceptible to local rock features. It was concluded that the rock hardness could be effectively predicted using only geochemistry, and the process of collecting P- and S-wave velocity for hardness prediction can be skipped.