Regional geochemical pattern recognition with multivariate correspondence cluster analysis in the Ravar area, Iran

Abstract

Multiple correspondence analysis and cluster analysis (MCACA) is developed in this paper, and then used to extract the main advantages of multiple correspondence analysis and hierarchical cluster analysis, and to unify the R- and Q-mode cluster analysis for a large data set. A systematic program to recognise the regional geochemical patterns is built up based on this method. With this program, the complex tasks for data interpretation could be achieved by simple processes, and important geochemical information could be displayed by a single diagram, i.e. the dendrogram. As a case study, the regional geochemical pattern recognition for the Ravar 1 : 100 000 sheet in the Kernam Province is discussed. The results show that many hidden geochemical patterns related to the lithologies, structures and prospecting targets are revealed by the geochemical map, and that the main geochemical patterns are related to certain geological patterns. By finding contrasts between geochemical patterns and geological patterns, the MCACA results assist the geological mapping in this area. Stream sediment geochemical data obtained in regional geochemical exploration in Iran provide useful information regarding geology and other factors, and the method described in this paper provides a new way to examine this type of resource.