Determination of alteration genesis and quantitative relationship between alteration and geochemical anomaly using support vector machines

In this research, support vector machine (SVM) as a supervised classification method has been used to explore the relationship between the geochemical anomaly and the surface alterations quantitatively in the Tanurcheh mineralization area. The Tanurcheh area has been located in the Khorasan Razavi province, Iran. This area has been considered as a high potential region for Cu and Au mineralization. The different mineralization processes of Au and Cu have unclearly been intertwined in this area and have created extreme surface alterations.Determination of the major origin of mineralization that has created strong alterations in this area is an important issue that can be addressed using a new proposed scenario. The relationship between the geochemical distribution map and the alteration zone was mathematically calculated using the proposed approach and then the geochemical anomaly map was predicted based on the alteration zones as an innovative achievement.In this paper, the Au and Cu geochemical data were divided into three classes, namely background, regional anomaly and local anomaly using the probability plot method. Two threshold values for Cu (۷۰ and ۳۰۰ PPM) and Au (۰.۱۳ and ۰.۴ PPM) were obtained by the probability plot method. Then the SVM was utilized to classify the geochemical samples using the ASTER images based on these obtained thresholds. The ASTER ۱۴-band images were used as features in this classification. Using this novel scenario, the relationships between the Au and Cu mineralization processes with the intensity of alterations were determined and therefore the origin of these alteration zones was clarified. The SVM classification indices of correct classification rate (CCR) and confusion matrix demonstrate the main origin of alterations is related to the Cu mineralization process in this area. The CCR indices obtained based on the Au and Cu thresholds are ۰.۶۶ and ۰.۸۵ respectively. It demonstrates the intensity of alterations has more been affected by the Cu mineralization process and there is a relatively good relationship between the alteration zone and the Cu geochemical distribution map. Finally, the geochemical anomaly and background maps were properly predicted using the SVM and the ASTER bands. This paper shows the new application of SVM as a powerful tool for the interpretation of geochemical anomaly and the intensity of alteration.

Keywords:

anomaly separation , ASTER images , Geochemical data , pattern recognition , Support vector machine