Machine learning assisted geological interpretation of drillhole data: Examples from the Pilbara Region, Western Australia

Abstract

Abstract In minerals exploration, routine drilling is performed and the data logged from these drillholes, including lithological composition, assays, and downhole geophysical measurements such as natural gamma logs, are used to create geological interpretations of the strata within each drillhole. A 3D geological model can be created by identifying corresponding stratigraphic boundaries within multiple drillholes. These models can be used for understanding the formation and the mineral endowment of a deposit. We introduce a system for producing stratigraphic interpretations of iron ore exploration drillholes in the Pilbara region in Western Australia. The algorithm firstly classifies each data modality independently for each geological interval, for example 2\u202fm, with classification results for each stratigraphic unit as output. These classifiers, for geological logging, assays, gamma logs, were trained on historical datasets over a wide range of strata in the Pilbara. The influence of each classifier can be adjusted according to the user’s preference, and a novel optimisation algorithm incorporates known geological features such as dykes, faults and thicknesses of various stratigraphic units, to objectively create the best fit interpretation of the geology. A geologist can then adjust this interpretation to include local knowledge. Manual interpretations of 396 drillholes from a high-grade iron ore deposit are compared to interpretations of the same hole prepared by the algorithm. Analysis of interval-by-interval interpretations, and basement geology demonstrate that without any human input, similar interpretations are produced while reducing manual effort.