Assessment of Various Fuzzy c-Mean Clustering Validation Indices for Mapping Mineral Prospectivity: Combination of Multifractal Geochemical Model and Mineralization Processes

摘要

This paper describes the application of an unsupervised clustering method, fuzzy c-means (FCM), to generate mineral prospectivity models for Cu ± Au ± Fe mineralization in the Feizabad District of NE Iran. Various evidence layers relevant to indicators or potential controls on mineralization, including geochemical data, geological-structural maps and remote sensing data, were used. The FCM clustering approach was employed to reduce the dimensions of nine key attribute vectors derived from different exploration criteria. Multifractal inverse distance weighting interpolation coupled with factor analysis was used to generate enhanced multi-element geochemical signatures of areas with Cu ± Au ± Fe mineralization. The GIS-based fuzzy membership function MSLarge was used to transform values of the different evidence layers, including geological-structural controls as well as alteration, into a [0-1] range. Four FCM-based validation indices, including Bezdek's partition coefficient (V_(Pe)) and partition entropy (V_(Pe)) indices, the Fukuyama and Sugeno (V_(FS)) index and the Xie and Beni (V_(Xb)) index, were employed to derive the optimum number of clusters and subsequently generate prospectivity maps. Normalized density indices were applied for quantitative evaluation of the classes of the FCM prospectivity maps. The quantitative evaluation of the results demonstrates that the higher favorability classes derived from V_(FS) and V_(xb) (N_d = 9-19) appear more reliable than those derived from V_(Pc) and V_(Pe) (N_d = 6.12) in detecting existing mineral deposits and defining new zones of potential Cu ± Au ± Fe mineralization in the study area.