Exploration geochemistry data-application for anomaly separation based on discriminant function analysis in the Parkam porphyry system (Iran)

Abstract

There are several statistical methodologies presented for separating anomalous values from background leading to determination of anomalous areas. These methods range from simple approaches to complicated ones. A common objective method for anomaly detection in geochemical exploration is target delineation by discriminant function analysis. Discriminant analysis (DA) is a multivariate statistical technique that classifies each observation into a specific group based on observed predictor variables and predefined groups. In the present study, to achieve this target, two populations of anomaly and background were determined using combination of Mahalanobis distance (MD) and median absolute deviation (MAD) method, using borehole data set. For this purpose, in the first step, Mahalanobis distance values should be determined and then MAD method should be applied on Mahalanobis distance values of boreholes data (grade of copper and molybdenum). Thus for separating anomaly from background of surface samples and determining anomaly areas, algorithm of DA method is applied on grade of Cu and Mo (surface samples). Results show that samples indicated by the DA methods as anomalous are more regular; less dispersed and are more accurate than other multivariate methods (e.g., MD method) and other univarite methods since anomalous samples are determined based on several variables. Finally, bivariate lithogeochemical map of the study area is provided for copper and molybdenum which has been prepared using DA method. In this map, the delineated Cu-Mo mineralization is closely associated with the defined zone of potassic alteration, which is also consistent with the field and microscopic observation of the Cu mineralization in this alteration zone. Moreover, it is associated with the phyllic alteration and is spatially conformable with the zone defined for it.