Abstract
We have developed an approach for automatic 3D geological mapping based on conversion of chemical composition of rocks to mineral composition by logical computation. It allows to calculate mineral composition based on bulk rock chemistry, interpolate the mineral composition in the same way as chemical composition, and, finally, build a 3D geological model. The approach was developed for the Kovdor phoscorite-carbonatite complex containing the Kovdor baddeleyite-apatite-magnetite deposit. We used 4 bulk rock chemistry analyses – Femagn, P2O5, CO2 and SiO2. We used four techniques for prediction of rock types – calculation of normative mineral compositions (norms), multiple regression, artificial neural network and developed by logical evaluation. The two latter became the best. As a result, we distinguished 14 types of phoscorites (forsterite-apatite-magnetite-carbonate rock), carbonatite and host rocks. The results show good convergence with our petrographical studies of the deposit, and recent manually built maps. The proposed approach can be used as a tool of a deposit genesis reconstruction and preliminary geometallurgical modelling.
Highlights
Methodology of spatial analysis has been developed significantly in recent years[1,2,3,4]
Most of three-dimensional geological modelling methods are based on cross-sections built manually using borehole data, and/or serious expert solutions accepted during the modelling process, e.g. refs 5 and 6
The method based on potential-field interpolation and geological rules[7] seems to be one of the best methods of 3D geological modelling that is less affected by subjective factors
Summary
Methodology of spatial analysis has been developed significantly in recent years[1,2,3,4]. Development of a priori geological rules (age relations and rock sequences) often presents some difficulty, especially in case of complicated deposits such as complex magmatic, metasomatic, hydrothermal, etc Such deposits usually have continuous transitions and uncertain age relations between different rock types, and questionable genesis. There is a problem: ore classification for geological and geometallurgical mapping must be based on the rock modal composition; but mineralogical studies of numerous samples are too expensive and time-consuming (only chemical composition of ores has been analyzed using a dense network of sampling points) This problem is important for mineral engineering, processing mineralogy and geometallurgy, because mineral processing is based on the properties of ore minerals. The proposed approach can be used as a tool of preliminary geometallurgical 3D-modelling
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