Deep-XFCT: Deep learning 3D-mineral liberation analysis with micro X-ray fluorescence and computed tomography
This provides a new robust method for 3D mineral liberation analysis in resource industries, environmental, and material sciences, addressing a gap in current 2D-based practices.
The paper tackles the problem of 3D mineral liberation analysis, which lacks non-destructive methods, by combining micro-CT with micro-XRF using deep learning, demonstrating successful semi-automated multi-modal analysis that differentiates feldspar from quartz in a crystalline magmatic rock.
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.