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The rapid development of X-ray micro-computed tomography (micro-CT) opens newopportunities for 3D analysis of particle and grain-size characterisation,determination of particle densities and shape factors, estimation of mineralassociations and liberation and locking. Current practices in mineralliberation analysis are based on 2D representations leading to systematicerrors in the extrapolation to volumetric properties. New quantitative methodsbased on tomographic data are therefore urgently required for characterisationof mineral deposits, mineral processing, characterisation of tailings, rocktyping, stratigraphic refinement, reservoir characterisation for applicationsin the resource industry, environmental and material sciences. To date, nosimple non-destructive method exists for 3D mineral liberation analysis. Wepresent a new development based on combining micro-CT with micro-X-rayfluorescence (micro-XRF) using deep learning. We demonstrate successfulsemi-automated multi-modal analysis of a crystalline magmatic rock where thenew technique overcomes the difficult task of differentiating feldspar fromquartz in micro-CT data set. The approach is universal and can be extended toany multi-modal and multi-instrument analysis for further refinement. Weconclude that the combination of micro-CT and micro-XRF already provides a newopportunity for robust 3D mineral liberation analysis in both field andlaboratory applications.

Deep-XFCT: Deep learning 3D-mineral liberation analysis with micro X-ray fluorescence and computed tomography