Deep-XFCT: Deep learning 3D-mineral liberation analysis with micro X-ray fluorescence and computed tomography
Patrick Kin Man Tung, Amalia Yunita Halim, Huixin Wang, Anne Rich,, Christopher Marjo, Klaus Regenauer-Lieb

TL;DR
Deep-XFCT combines micro-CT and micro-XRF with deep learning to enable non-destructive, 3D mineral liberation analysis, overcoming limitations of traditional 2D methods and improving accuracy in resource and environmental sciences.
Contribution
This paper introduces a novel deep learning-based method integrating micro-CT and micro-XRF for 3D mineral analysis, a significant advancement over existing 2D approaches.
Findings
Successful semi-automated multi-modal analysis demonstrated
Overcomes differentiation challenges in micro-CT data
Provides a new non-destructive 3D mineral analysis technique
Abstract
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…
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