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Application of DomainMCF to marble quality classification
Machine learning is constantly gaining ground in the mining industry. Machine learning based systems take advantage of the computing power of personal, embedded and cloud systems of today to rapidly build models of real processes, something that would have been impossible or extremely time consuming a couple of decades ago. The widespread access to the internet and the availability of cheap and powerful cloud computing systems, led to the development and acceptance of tools used to automate processes such as resource modelling or mine scheduling and optimisation, using machine learning methodologies. The domain modelling system discussed in this presentation, called DomainMCF, has been developed by Maptek, and is based on artificial neural network technology. It can be used to model the spatial distribution of discrete domain values from a set of samples. In the application presented here, DomainMCF is used to model the spatial distribution of marble quality categorical parameters (such as texture, colour, and density of tectonic features inside the marble volume), and the results are combined to produce a final marble quality classification using drillhole and quarry face samples from an operational marble quarry in NE Greece. The process is controlled by upper and lower boundary surfaces, acting as vertical limits of the classification. DomainMCF was made available for this study as a cloud processing service through an early access program for individuals or companies who are interested in testing its capabilities and suitability in various modelling scenarios and geological settings. The resulting marble product classifications are compared to those produced by the already established classification system that is based on a more conventional estimation method. The produced results show that DomainMCF can be effectively applied to the modelling of marble quality spatial distribution and similar domaining problems.

May 19, 2021 02:30 PM in London

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