Characterization of Geochemical Pattern Using Principal Component Analysis (PCA) and Fuzzy C-mean Clustering (FCMC) For Enhancement of Weak Anomalies (Gulan area, E-Azerbaijan, Iran)

Optimization of geochemical anomalies needs an orientation survey in which one of its important aspects is selecting an advanced data processing method. The main objective of this study is to recognize the blind and mineralization zones by employment of new processing techniques in order to establish an optimized exploration tool and reliable geochemical pattern for potentially promising areas in Gulan. In this respect 233 stream sediment samples were collected and analyzed for Cu, Pb, Zn, Mo, Co, Ni, Cr, As, and Y. The anomalous zones were detected by using PCA&FCMC methods. The FCMC results revealed Cu, Mo anomalous zones in Garachilar area. It shows secondary halos separation of Cu and Mo probably due to transportation of Mo in the form of molybdates by acidic solution around outcrops, and consequently their adjoined redeposition. Application of Fuzzy logic Based FCMC shows the emplacement of Cu and Mo in the same cluster and overlapping of their anomalies which indicate their paragenetic relation in the ore bearing solution. Comparative study of the methods (FCMC&PCA) revealed some how similar results in detecting Garachilar anomalies. But the PCA results not only indicate Garachilar as promising zones but also could detect western part of Lutkeh and blind anomalies of Namnig in the same trend of NW-SE. This study indicates that geochemical pattern detected by PCA is more effective in enhancement of halos and blind anomalies than FCMC. Moreover, the characterization of geochemical pattern by PCA can be optimized more precisely in eliminating lithological effect and its results can be used successfully as prospecting tool in the area.