Article · Applied Computing and Geosciences · July 17, 2024

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Aida Zhexenbayeva, Nasser Madani, Philippe Renard, Julien A. Straubhaar

Abstract

Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.

Geological Modeling and AnalysisGeochemistry and Geologic MappingMining Techniques and Economicsmultiple-point geostatisticsvein-type gold depositsradial basis functions

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Abstract

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