A Machine Learning Classification Approach to Geotechnical Characterisation Using Measure-While-Drilling Data

Due to limited exploration drilling and analogue mapping, bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis. The Measure-While-Drilling (MWD) system uses sensors to collect drilling data from mining blast hole drill rigs. Historically, MWD studies have focused on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including Stratigraphic Unit, Rock/Soil Strength, Rock Type, Geological Strength Index, and Weathering properties. Feature selection algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Random Forests (RFs), Linear Discriminant Analysis, and Naive Bayes. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI in real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis.