Kohonen neural network and factor analysis based approach to geochemical data pattern recognition

摘要

Kohonen neural network (KNN) and factor analysis are applied to regional geochemical pattern recognition for a Pb-Zn-Mo-Ag mining area around Sheduolong in Qinghai Province, China. Prior to factor analysis, the geochemical data are classified by KNN. The results demonstrate that the 4-factor model accounted for 67% of the variation in the data. Factor F1, a Pb-Zn-Mo factor and Factor F4, an Au-Ag factor, correlates with monzonitic granite intrusions and particularly with Pb-Zn-Mo-Ag mineralization within those rocks. Factor F2, an As-Co factor, correlates with metamorphic rocks of paleoproterozoic Baishahe formation. Factor F3, a Bi-Cu factor, correlates with granodiorite intrusions. The factor score maps suggest a revised location of faults and their mineralization significance in coarse geological map. The approach not only effectively interprets the geological significance of the factors, but also reduces the area of exploration targets.