Mineralogic SEM-EDS automated mineralogy: a preliminary study on the quantitative mineral classification

The advent of ZEISS Mineralogic Mining and its utilization of the fully quantitative EDS mineral classification methodology has resulted in substantial improvements in mineral classification accuracy and elemental assay on SEM-EDS automated mineralogy solutions. This new solution uses the wt.% contribution of each element present (mineral stoichiometry) to classify the mineral phases, thus resulting in improved accuracy in mineral discrimination and assay calculations. For this study, one random field of view (FOV) was analysed 20 times with the average number of counts per EDS pixel (collected on a 10 μm stepping interval) ranging from 1,000 to 3,500 with 500 count increments. The analyses were carried out on a single sample from the Jabali Zn-nonsulfides mining area [1]. Additional analyses at 500 counts and 5,000 counts, which were repeated 10 times each on the one FOV. The aim of the study was to identify the optimal balance of analytical speed against accuracy of EDX quantification, and to test the repeatability of results Previous studies on the Jabali ore deposit [1] showed that the mineralogy is quite complex and consists of: Zn-carbonate and hydrosilicate (smithsonite, hemimorphite), Pb-carbonate and sulfate (cerussite, anglesite) mixed with remnant sulfides (sphalerite and galena), and Fe-hydroxides (mainly goethite). The host rock is dolomite that can be locally Zn- and Fe-enriched. The initial results from this small experiment suggest that within the Zn-carbonate samples, EDS analysis below c. 2000 counts produces inadequate results when trying to track Zn and Fe distribution. EDS analysis at c. 1000 counts per pixel produced average bulk data with over 12% unclassified EDS pixels, 63% pure dolomite and only 22% Fe-dolomite. An increase in counts per pixel resulted in a dramatic change; c. 3,000 counts per EDS pixel provided average bulk mineral data with only 6% unclassified EDS pixels, 35% pure dolomite and 55% Fe-dolomite This significant change in reported mineralogy is due to the presence of Fe within dolomite at between typically 1-5 wt% which is undetectable at 1000 counts. Further inspection of the data using the assay directly measured by Mineralogic Mining for Fe shows that the average Fe assay at c. 1,000 counts is 1.39%, whilst a c. 3,000 count analysis provides an average assay value of 2.52%. Relative standard deviation for the c. 1,000 counts is 16.28%, while was only 4.35% for c. 3,000 counts. To further outline this trend, several analyses with c. 5,000 counts per EDS pixel were carried out; this data showed how average bulk data of unclassified EDS pixels is 5%, for pure dolomite 25% and Fe-dolomite 66%. Fe assay produced an average assay value of 2.74% with a relative standard deviation of 1.69. Time length per analysis must also be considered, as higher counts per EDS pixel mean longer analysis time. However, this study outlines the importance of analysis optimization and balancing data quality with analysis time. In conclusion, this case study outlines how, when using SEM-EDS automated mineralogy, higher counts per EDS pixel are required to produce more precise and representative data. Failure to use a sufficient number of counts and non-quantitative EDS can result in mineral phase mis-classification, incorrect tracking of elemental distribution and unrepresentative calculated assay values.