DomainMCF

Building the best possible 3D model from observed geological data is critical before grade estimation is performed. For many decades, 3D modelling of faults has been a challenging task. Currently, explicit or implicit modelling techniques are widely used to model faults.

In real-world data sets, missing values are unavoidable for various reasons. These missing values are typically represented by NaNs, default placeholders, or simply left as blank entries. Depending on the extent of missing data, this can significantly reduce the performance of statistical methods.

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.

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.

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.

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.