Adaptive ore grade estimation method for the mineral deposit evaluation

Authors:
Xiao-Li Li;Yu-Ling Xie;Qian-Jin Guo;Li-Hong Li
Affiliations:
Civil and Environmental Engineering School, University of Science and Technology Beijing, Beijing, 100083, China;Civil and Environmental Engineering School, University of Science and Technology Beijing, Beijing, 100083, China;State Key Laboratory of Molecular Reaction Dynamics, Institute of Chemistry, The Chinese Academy of Sciences, Beijing, 100190, China;College of Vehicle and Motive Power Engineering, Henan University of Science and Technology, Luoyang, China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

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Abstract

Ore grade estimation is one of the most key and complicated aspects in the evaluation of a mineral deposit. Its complexity originates from scientific uncertainty. This paper introduces a new nonlinear and adaptive method to the problem of ore grade estimation, which is based on the Wavelet Neural Network (WNN) approach, and is designed to receive drill hole information from an orebody and perform ore grade estimation on a block model basis. The nonlinear ore grade estimation method combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANNs) provides fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modeling skills. A number of case studies have been carried out using the new ore grade estimation method. The results obtained and the overall functionality of the method prove that Wavelet Neural Networks can offer a fast and robust grade estimation technique and a valid alternative to well established methodologies in this area.