Artificial intelligence and big data analytics in mining geomechanics

Abstract

Mining geomechanics presents specific challenges to application of the closely-related methods of artificial intelligence (AI), big data, predictive analytics, and machine learning. This is because successful use of these techniques in geotechnical engineering requires four-dimensional (x, y, z, t) data integration as a prerequisite, and 4D data integration is a fundamentally difficult problem. This paper describes a process and software framework that solves the prerequisite 4D data integration problem, setting the stage for routine application of AI or machine learning methods. The work flow and software system brings together structured and unstructured data and interpretation from drill-hole data to all types of geological, geophysical, rock property, geotechnical, mine production, fixed plant, mobile equipment, and mine geometry data, to provide a data fusion capability specifically aimed at applying machine learning to rock engineering problems. The system does this by maintaining 3D earth model and 4D mine model geometrical data structures, upon which multiple data-sets are projected, interpolated, upscaled, downscaled, or otherwise processed appropriately for each data type so that the variables of importance for each problem can be co-located in space and time, a requirement for the application of any analytics algorithm. Documents and files can be stored, managed, and linked to data and interpretation to provide relevant metadata and contextual links, providing the platform required for AI solutions. The system rationale and structure are described with reference to specific AI challenges in rock engineering.