Abstract:

Independent component analysis (ICA) has the advantage of handling the higher-order statistics which are ignored by principle component analysis (PCA). However, decomposed signals by ICA have the character of indeterminacy for the sequences, signs and scales which are of great importance in mineral (i.e. geochemistry) data. Therefore, a new mineral resources prediction approach based on robust ordinal independent component analysis(ROICA) is proposed.  Firstly, the mineral data are processed by robust algorithm. Then, a ICA method is applied to the data and factor analysis is introduced to eliminate the indeterminacy among the decomposed independent components. The proposed new algorithm is applied to mineral resources prediction in Shuangren ore district, Guangdong province. Compared with the traditional meanvariance method, the chemical elements (Au and Ag) decomposed by the ROICA accord well with the practical distribution of gold and silver ore bodies while combination of chemical elements (e.g. Cu, Pb and Zn) has no indication significance as they have no association with gold and silver ore bodies. This method can be used for prediction of mineral resources.

Key words: mineral resources prediction, independent component analysis, factor analysis, robust preprocessing