A PCA-AHC Approach to Provenance Studies of Non-Ferrous Metals with Combined Pb Isotope and Chemistry Data

Abstract

This paper discusses the applicability of the Principal Component Analysis-Agglomerative Hierarchical Clustering (PCA-AHC) approach to provenance studies of nonferrous metals using combined Pb isotope and chemistry data. Pb isotopic ratios were converted to the natural abundance of individual isotopes and then to weight units. Next, all relevant variables (Pb isotopes and trace elements) were processed with PCA and AHC to examine the relationships between observations. The method is first verified on three literature-based case studies (1, 2, and 3). It is argued that, as is the case in archaeological iron provenance studies, the PCA-AHC method is also viable for non-ferrous metals. This method can greatly facilitate research, compared to conventional biplots with ratios of Pb isotopes and trace elements. Additionally, PCA-AHC can become part of the initial deposit selection process, and it can help clarify less obvious classification cases. The main problem with a practical application of this approach is insufficient deposit datasets with complete Pb isotopic and chemistry data. In such cases, it is possible to use the PCA-AHC method separately on Pb isotopic and chemistry data and then to compare and contrast results. Alternatively, the proposed approach can be used solely with Pb isotopic data. This application is shown in two additional case studies (4 and 5), which demonstrate the method’s application for tracing artefacts to their parent ores using datasets with a few thousand observations.