A comprehensive survey on machine learning applications for drilling and blasting in surface mining

Abstract

Drilling and blasting operations are pivotal for productivity and safety in hard rock surface mining. These operations are restricted due to complexities such as site-specific uncertainties, safety risks, and environmental and economic constraints. Machine Learning (ML) is a transformative approach to tackle these complexities resulting in significant cost reductions. ML applications can reduce overall blasting costs by up to 23% and decrease the amount of explosives by as much as 89% compared to traditional methods. This survey presents a comprehensive review of how ML can be applied to optimize drill and blast designs while accounting for its operational challenges. Our research highlights the difficulties in collecting quality site-specific data, the complexity of interpreting this data into insightful information, the selection of ML models relating to mining objectives, and the need for established methods to assess blast efficiency quantitatively. We provide a synthesis of ML model development practices in drilling and blasting and demonstrate the value of ML methodologies. Based on our survey, we present actionable recommendations for developing ML methodologies to improve safety, reduce costs, and enhance efficiency in drilling and blasting processes. This includes establishing standardized data schematics, multiobjective model optimization, and comprehensive evaluation metrics. These benefits can guide mine management and engineers to adopt ML techniques and improve on-ground operational practices. This survey aims to serve as a resource for both practitioners and researchers shaping the future research direction in ML applications for drilling and blasting practices.