Uncertainties in quantitative mineralogical studies using scanning electron microscope-based image analysis

Scanning electron microscope-based automated mineralogy studies are readily associated with quantitative results, providing one of the foundations for geometallurgical studies. Despite the importance of quantitative data for such studies, and efforts to reduce statistical errors, the reporting of uncertainties is rare. This contribution illustrates how bootstrap resampling can be used to provide robust estimates of statistical uncertainties for the modal mineralogy, metal deportment and all relevant textural attributes of a sample, or series of samples. Based on a case study of the Bolcana Au-Cu porphyry deposit in the South Apuseni Mountains, Romania, the impact of insufficient sampling statistics on quantitative mineralogical studies is illustrated. Quantitative analyses of the mineralogy and microfabric of milled ore samples from seven 40 m drill core intervals from the Bolcana Prospect were conducted using a Mineral Liberation Analyser (MLA), complemented by electron probe micro-analysis. Bootstrap resampling was then applied to assess how many grain mount surfaces should be analysed to achieve statistically robust results for both Cu and Au mineralogy, deportment and textural attributes. Despite variable mineralogy, grades and mineralisation styles, estimated statistical uncertainties on Cu deportment are consistently low. In contrast, uncertainties for Au deportment are so high that most reported values for important characteristics are statistically meaningless. This is mainly attributed to the pronounced nugget effect for Au mineralisation, exacerbated by the small sample size analysed by MLA. An unfeasible number of measurements would be necessary to provide robust figures for the deportment of minor/trace elements and minerals, along with other tangible mineralogical properties, such as mineral associations. The results of this case study demonstrate that statistical uncertainties need to be carefully incorporated when considering the results of automated mineralogical studies and their impact on geometallurgical models. This is particularly relevant for studies of precious metal ores.