Quantitative Characterisation of Grain Morphology utilising Computer Vision and µXRF

A useful advantage of the non-destructive approach to sample characterisation via µXRF-EDS is that samples do not require mounting and polishing in epoxy resin before analysis. In the case of heavy mineral concentrates, loose grains can be analysed and identified based on their EDS spectra, and the true grain morphology characteristics can be determined for each identified grain.

Here we discuss the results of a case study that delineates and characterises individual heavy mineral concentrate grains ranging from 300 to 100 microns in diameter within sample sizes ranging from hundreds to thousands of grains. Using high resolution images taken prior to analysis, we apply a grain segmentation model to identify the boundaries of each individual grain. This is then used in conjunction with the mineral classification results via µXRF-EDS to correlate quantitative morphological data (grain size and shape) with quantitative mineralogical data a grain-by-grain basis.

This approach has been tested at scale in a robot-enabled commercial laboratory. It is a significant improvement in analytical precision and operational efficiency in joint characterisation of mineralogy and morphology of samples for geologists, geometallurgists, and process engineers in the environmental and mining industries.