Empress Catherine II Saint Petersburg Mining University (St. Petersburg, Russia)

Zakharova A. A., Assistant, Candidate of Geological and Mineralogical Sciences, zakharova.alena27614@gmail.com

Herzen University (St. Petersburg, Russia)

Voytekhovsky Yu. L., Head of Chair, Doctor of Geological and Mineralogical Sciences, Professor, vojtehovskijj@herzen.spb.ru

This article provides further details for the authors’ mineralogical and technological research in the field of predicting ore processing results through statistical structural analysis of intergranular intergrowths in petrographic thin sections and polished sections. This approach is based on the premise that ore mineral separation comes down to destruction of its contacts with the associated minerals. Using crystalline schists from a gold deposit as an example, a relationship has been identified between ore processing performance, as measured by the Bond and A×b indices, and the structural types of such schists using the classification previously proposed by the authors. The strongest ores in terms of the Bond index were represented by structural types S₂² and S₃². Higher index values are consistent with higher proportions of matrix–matrix contacts, mainly for mica. The strongest ores in terms of the A×b parameter were represented by structural types S₂¹ and S₃¹. The strength of the schists decreases with increasing grain sizes and proportions of monomineral contacts. The processing performance is also influenced by the structural positions of individual minerals. The results suggest that microscopic examination with statistical analysis of structures should be used at the early stages of mineralogical and technological mapping to predict the physical and mechanical properties of ores during processing. Like any statistical study, it requires both representative thin sections to establish samples characteristics and a representative collection of ores with varying structural types. The method developed by the authors may be used in geological exploration and mineralogical and technological work aided by computerized optical structure analyzers. This will enable the prediction of ore processing performance based on structural types of the ores in the early stages of deposit mapping.

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