  The rapid development of X-ray micro-computed tomography (micro-CT) opens new
opportunities for 3D analysis of particle and grain-size characterisation,
determination of particle densities and shape factors, estimation of mineral
associations and liberation and locking. Current practices in mineral
liberation analysis are based on 2D representations leading to systematic
errors in the extrapolation to volumetric properties. New quantitative methods
based on tomographic data are therefore urgently required for characterisation
of mineral deposits, mineral processing, characterisation of tailings, rock
typing, stratigraphic refinement, reservoir characterisation for applications
in the resource industry, environmental and material sciences. To date, no
simple non-destructive method exists for 3D mineral liberation analysis. We
present a new development based on combining micro-CT with micro-X-ray
fluorescence (micro-XRF) using deep learning. We demonstrate successful
semi-automated multi-modal analysis of a crystalline magmatic rock where the
new technique overcomes the difficult task of differentiating feldspar from
quartz in micro-CT data set. The approach is universal and can be extended to
any multi-modal and multi-instrument analysis for further refinement. We
conclude that the combination of micro-CT and micro-XRF already provides a new
opportunity for robust 3D mineral liberation analysis in both field and
laboratory applications.
