Investigating the Relationship between Geochemistry, Leeb Rebound Hardness, and Cerchar Abrasivity Index

Rock hardness and abrasivity are among the most crucial properties that can significantly impact the interaction between rocks and mechanical tools in different parts of geoengineering projects. Accurate estimation of these properties is essential for a better understanding and optimization of geoengineering operations. Hence, the main aim of this study was to develop different machine learning (ML) models based on the geochemical measurements for predicting rock hardness and abrasivity. To do this, 159 rock samples were collected from a gold mine, and portable X-ray fluorescence spectrometry (pXRF), Leeb rebound hardness (LRH), and Cerchar abrasivity index (CAI) tests were performed on the collected rock samples. Three different ML algorithms, including random forest regressor (RFR), support vector regression, and gradient boosting regressor, were applied to develop predictive models for LRH and CAI separately. Considering the fact that the geochemical data are of the compositional type, two scenarios were followed: developing predictive models based on the original data obtained from the pXRF and the centered log-ratio (Clr) transformed data, resulting in the development of six predictive models for LRH and CAI. The performance assessment of the developed predictive models showed that RFR models outperformed the other two ML algorithms in predicting LRH and CAI. In addition, the developed models based on the original data demonstrated a better performance in both cases of LRH and CAI than the trained model based on Clr data. The result indicates that integrated pXRF measurements and RFR technique have strong potential to be used for practical and efficient rock materials characterization during exploration and extraction processes.