New Strategies in Archaeometric Provenance Analyses of Volcanic Rock Grinding Stones : Examples from Iulia Libica (Spain) and Sidi Zahruni (Tunisia)

Lluís Casas Duocastella, Anna Anglisano, Àfrica Pitarch Martí, Ignasi Queralt, César Carreras Monfort, Boutheina Fouzai

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Archaeometry can help archaeologists in many ways, and one of the most common archaeometric objectives is provenance analysis. Volcanic rocks are often found in archaeological sites as materials used to make grinding tools such as millstones and mortars or as building materials. Petrographic characterization is commonly applied to identify their main mineralogical components. However, the provenance study of volcanic stones is usually undertaken by comparing geochemical data from reference outcrops using common descriptive statistical tools such as biplots of chemical elements, and occasionally, unsupervised multivariate data analysis like principal component analysis (PCA) is also used. Recently, the use of supervised classification methods has shown a superior performance in assigning provenance to archaeological samples. However, these methods require the use of reference databases for all the possible provenance classes in order to train the classification models. The existence of comprehensive collections of published geochemical analyses of igneous rocks enables the use of the supervised approach for the provenance determination of volcanic stones. In this paper, the provenance of volcanic grinding tools from two archaeological sites (Iulia Libica, Spain, and Sidi Zahruni, Tunisia) is attempted using data from the GEOROC database through unsupervised and supervised approaches. The materials from Sidi Zahruni have been identified as basalts from Pantelleria (Italy), and the agreement between the different supervised classification models tested is particularly conclusive. In contrast, the provenance of the materials from Iulia Libica remained undetermined. The results illustrate the advantages and limitations of all the examined methods.
Original languageEnglish
Article number639
Number of pages25
JournalMinerals
Volume14
Issue number7
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Archaeometry
  • Volcanic stone
  • Grinding tools
  • Provenance studies
  • Supervised methods
  • Machine learning
  • Clustering
  • XRF

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