A nonlinear grade estimation method based on Wavelet Neural Network

Abstract

Grade estimation is one of the most complicated aspects in mining. Its complexity originates from scientific uncertainty. This paper introduces a nonlinear Wavelet Neural Network (WNN) approach to the problem of ore grade estimation. The nonlinear WNN method combing the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) provide fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modeling skills. The WNN grade estimation method has been tested on a number of real deposits. The result shows that the WNN has advantages of rapid training, generality and accuracy grade estimation approach. It can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation.