Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

New Strategies in Archaeometric Provenance Analyses of Volcanic Rock Grinding Stones

Version 1 : Received: 13 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (09:29:46 CEST)

How to cite: Casas, L.; Di Febo, R.; Anglisano, A.; Pitarch Martí, Á.; Queralt, I.; Carreras, C.; Fouzai, B. New Strategies in Archaeometric Provenance Analyses of Volcanic Rock Grinding Stones. Preprints 2024, 2024050939. https://doi.org/10.20944/preprints202405.0939.v1 Casas, L.; Di Febo, R.; Anglisano, A.; Pitarch Martí, Á.; Queralt, I.; Carreras, C.; Fouzai, B. New Strategies in Archaeometric Provenance Analyses of Volcanic Rock Grinding Stones. Preprints 2024, 2024050939. https://doi.org/10.20944/preprints202405.0939.v1

Abstract

Archaeometry can help archaeologists in multiple instances, one of the common archaeometric goals are the provenance analyses. Volcanic stones appear frequently in archaeological sites as materials used to build grinding tools like millstones and mortars or as building materials. Petrographic characterization is commonly applied to identify their main mineralogical components. However, provenance of volcanic stones is usually undertaken by comparison with geochemical data from reference outcrops using common descriptive statistical tools such as biplots of chemical elements, and unsupervised multivariate data analysis like principal component analysis (PCA). 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 every possibly provenance class to be able to train the used classification algorithms. The existence of comprehensive collections of published geochemical analyses of igneous rocks enables the use of the supervised approach for 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.

Keywords

archaeometry; volcanic stone; grinding tools; provenance studies; supervised methods; machine learning; clustering; XRF

Subject

Environmental and Earth Sciences, Geochemistry and Petrology

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.