3D modelling of a mineral deposit using drill core hyperspectral data

Drill core samples have been traditionally used by the mining industry to make resource estimations and to build geological models. The hyperspectral drill core scanning has become a popular tool in mineral exploration because it provides a non-destructive method to rapidly characterise structural features, alteration patterns and rock mineralogy in a cost effective way.

Typically, the hyperspectral sensors cover a wide spectral range from visible and near-infrared (VNIR) to short and long wave infrared (SWIR and LWIR). The spectral features in this range will help to characterize a large number of mineral phases and complement the traditional core logging techniques. The hyperspectral core scanning provide mineralogical information in a millimetre scale for the entire borehole, which fills the gap between the microscopic scale of some of the laboratory analytical methods or the sparse chemical assays and the meter scale from the lithological descriptions.

However, applying this technique to the core samples of an entire ore deposit results in big datasets. Therefore, there is the need of a workflow to build a 3D geological model conditioned by the data applying suitable data reduction methods and appropriate interpolation techniques.

This contribution presents a case study in the combination of traditional core logging and hyperspectral core logging for geological modelling. To attain mineral and alteration maps from the hyperspectral data, unsupervised classification techniques were applied generating a categorical data set. The amount of data was reduced by the application of a domain generation algorithm based on the hyperspectral information. The domain generated by the algorithm is a compositional categorical data set that was then fed to condition the application of stochastic Plurigaussian simulations in the construction of 3D models of geological domains. This technique allows to simulate the spatial distribution of the hyperspectral derived categories, to make a resource estimation and to calculate its associated uncertainty.