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 pre-requisite, and 4D data integration is a fundamentally difficult problem. This paper describes a process and software framework that solves the pre-requisite 4D data integration problem, setting the stage for routine application of AI or machine learning methods. The workflow and software system brings together structured and unstructured data and interpretation from drillhole 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.
Highlights
Most people are aware of the artificial intelligence (AI) technology revolution
From self-driving cars to medical, financial, and marketing applications, we have been exposed to its predictive power. Why have these methods not yet had a significant impact on understanding or forecasting mining geomechanics outcomes? The rewards of AI should be immense as mines get deeper and forecasting of stress-related or other rock behaviour becomes a limiting factor on safety and production
A framework for successful application of AI in rock engineering A system, Geoscience INTEGRATOR (McGaughey et al, (2017), has been created that provides simple computation of the variables required to address the application of AI to mining geomechanics problems, and provides a real, working data-structure definition to the notion of a 4D mine model
Summary
Most people are aware of the AI technology revolution. From self-driving cars to medical, financial, and marketing applications, we have been exposed to its predictive power. In mining geomechanics, its application is far from simple The reason for this is that the condition being predicted, such as the location and timing of a geotechnical hazard (including rockfall, rockburst, or slope failure, seismic event probability forecasting, ore dilution forecasting, or drawpoint hang-up prediction), may be related to known factors (e.g. geology, rock mass properties, fault structures, mine geometry, stress, extraction, production, stope sequencing, deformation, seismicity, blasting, and support). Those factors are in many cases not estimated quantifiable variables at the location where the prediction is required. 3. establish the relative importance of individual data types in understanding future behaviour
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