Using multiple-point geostatistics for geomodeling of a vein-type gold deposit
Zhexenbayeva A. Madani N. Renard P. Straubhaar J.
September 2024Elsevier B.V.
Applied Computing and Geosciences
2024#23
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.
Cascade modeling , Direct sampling , Gold deposit , Multiple-point statistics , Probabilistic approach , Resource modeling , Sequential Gaussian simulation , Training image
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School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
Stochastic Hydrogeology Group, University of Neuchâtel, Neuchâtel, Switzerland
School of Mining and Geosciences
Stochastic Hydrogeology Group
10 лет помогаем публиковать статьи Международный издатель
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