GEMNET II – A Neural Ore Grade Estimation System

This paper describes a neural ore grade estimation system developed at the AIMS Research Unit of the University of Nottingham. GEMNET II is a modular neural network system designed to receive drillhole information from an orebody and perform ore grade estimation on a block model basis. The aims of the system are to provide a valid alternative to conventional grade estimation techniques while reducing considerably the time and knowledge required for development and application. GEMNET II is fully integrated inside VULCAN, one of the leading software packages for resource modelling, allowing for advanced visual validation of the grade estimation process. The system uses parts of the SNNS v4.1 neural network simulator for the development and training of the neural network modules. A number of case studies have been carried out using GEMNET II. The results obtained and the overall functionality of the system prove that neural networks can offer a fast and robust grade estimation technique and a valid alternative to well established methodologies in this area.