Probabilistic Modeling of Lateritic Nickel Mineral Resources

Aida Zhexenbayeva a , Nasser Madani, Philippe Renard, Julien Straubhaar

School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
Stochastic Hydrogeology Group, University of Neuchˆ atel, Neuchˆ atel, Switzerland

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

Keywords:

Cascade modeling ,Multiple-point statistics, Direct sampling, Training image, Gold deposit, Resource modeling, Sequential Gaussian simulation
Probabilistic approach.