Integrating Machine Learning Techniques with Measurement-While-Drilling Data for Subsurface Characterization in Open-Pit Mines

Abstract

Measurement-While-Drilling (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, the complexity and variability of drilling signals require analytical workflows that go beyond traditional threshold-based interpretation. This thesis integrates machine learning techniques with MWD data to improve subsurface characterization in two open-pit mines, each influenced by different operational and geological conditions. The first research component focuses on identifying zones of disturbed or weakened ground by detecting drilling behavior indicative of voids and compromised rock mass conditions in a mine affected by historic underground workings. The second component applies a structured data preparation and analysis workflow to develop predictive models for lithology and penetration rate in a separate open-pit operation, demonstrating how MWD data can support geological classification and drilling performance evaluation. Across both studies, the research highlights the importance of exploratory data analysis (EDA), feature engineering, and appropriate model selection. The results show that machine learning offers a scalable and effective way to extract meaningful information from MWD data, enhancing both geotechnical hazard detection and geological modeling. These findings demonstrate the value of integrating modern data science methods into mining workflows, contributing to safer and more informed operational decision-making.