Editorial for Special Issue “Applications of SEM Automated Mineralogy: From Ore Deposits over Processing to Secondary Resource Characterization”

Schulz, Bernhard

doi:10.3390/min10121103

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…Mineral liberation analysis has also been used in research and industry for particle-and grain-size characterisation [3], determination of particle densities and shape factors [4], estimation of mineral associations, liberation and locking [5] and theoretical grade-recovery curves [6]. The technique has now been extended for characterisation of ore deposits to secondary resource characterisation of tailings [7], and applications in the petroleum industry for the characterisation of sediments [8], rock typing, stratigraphic refinement and reservoir characterisation [9].…”

Section: Introductionmentioning

confidence: 99%

Deep-XFCT: Deep Learning 3D-Mineral Liberation Analysis with Micro-X-ray Fluorescence and Computed Tomography

et al. 2022

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Quantitative characterisation through mineral liberation analysis is required for effective minerals processing in areas such as mineral deposits, tailings and reservoirs in industries for resources, environment and materials science. Current practices in mineral liberation analysis are based on 2D representations, leading to systematic errors in the extrapolation to 3D volumetric properties. The rapid development of X-ray microcomputed tomography (μCT) opens new opportunities for 3D analysis of features such as particle- and grain-size characterisation, determination of particle densities and shape factors, estimation of mineral associations, and liberation and locking. To date, no simple non-destructive method exists for 3D mineral liberation analysis. We present a new development based on combining μCT with micro-X-ray fluorescence (μXRF) using deep learning. We demonstrate successful semi-automated multimodal analysis of a crystalline magmatic rock by obtaining 2D μXRF mineral maps from the top and bottom of the cylindrical core and propagating that information through the 3D μCT volume with deep learning segmentation. The deep learning model was able to segment the core to obtain reasonable mineral attributes. Additionally, the model overcame the challenge of differentiating minerals with similar densities in μCT, which would not be possible with conventional segmentation methods. The approach is universal and can be extended to any multimodal and multi-instrument analysis for further refinement. We conclude that the combination of μCT and μXRF can provide a new opportunity for robust 3D mineral liberation analysis in both field and laboratory applications.

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