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DomainMCF

Conference Papers

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Deep Learning – A New Paradigm for Orebody Modelling

This 2019 paper introduces you to orebody modelling of the future. Learn how machine learning allows rapid generation of domained orebody models, including the estimation of multiple numeric variables and uncertainties, directly from drillhole data.

Harnessing Data Complexity – How Machine Learning Applies All Project Data for Accurate Resource Modelling

This paper discusses the factors accentuating complexity in deposit modelling: data diversity, structural controls, chemistry, data volumes, process workflows and external non-geological constraints. A case study illustrates the risk of ignoring complexity, which can result in an overly simplified geological model.

Application of Machine Learning to Resource Modelling of a Marble Quarry

This 2021 paper outlines the DomainMCF modelling approach to the spatial distribution of marble quality categorisation parameters and compares it to marble product classifications generated by the conventional estimation method.

Machine Learning in Resource Geology – Why Data Quality is Critical

This paper outlines the results of the Nova-Bollinger modelling trial conducted by the mine geology team for mineral resource estimation. Advantages and disadvantages of implicit modelling and machine learning methods for preparing mineral resource estimation domains are covered.