GEMNet, (Grade Estimation using Mapping Networks), is a
grade/reserve estimation software system that uses the artificial
intelligence technique known as neural networks to perform reserve
estimates from both two- and three-dimensional data samples from a
mineral deposit. The system is the result of research carried out over the
past three years into the feasibility of using neural networks for reserve
estimation at the AIMS Research Unit in the Department of Mineral
Resources Engineering at the University of Nottingham. This paper describes the architecture of the GEMNet system including
details of the neural network components of the system. The performance
of the GEMNet system is then compared to several other widely used
reserve estimation techniques on two reserve estimation examples. The
results produced by the GEMNet system compare favourably with more
conventional estimation techniques, but require fewer assumptions to be
made about the form of the data used, and do not require the use of
complex mathematical modelling.