Machine Learning-based Provenance Analysis of Portuguese Chalcolithic Variscite: New Insights from n=182 Green Phosphate Beads.

Derived from the presentation entitled “Using AI interpretability techniques as a method for origin determination of prehistoric green phosphate beads in Europe” given at the annual conference of Computer Applications and Quantitative Methods in Archaeology (CAA) 2025 in Athens, this work employs a newly developed machine learning-based framework to determine the geological provenance of n=182 prehistoric green phosphate artefacts from four Portuguese archaeological sites.
The framework named VORTEX (Variscite ORigin TEchnology X-ray based) implement a scalable, data-driven approach that integrates portable X-ray fluorescence (p-XRF) analysis, machine learning (ML), and eXplainable Artificial Intelligence (XAI) techniques to address critical limitations in current provenance models, including insufficient geological source characterization, lack of scalability, and absence of probabilistic uncertainty quantification.

Our results reinforce emerging perspectives on prehistoric variscite exchange networks, corroborating the absence of evidence for Pico Centeno as a source for Portuguese sites while confirming Aliste as the principal supplier for Portuguese  Chalcolithic assemblages, thereby strengthening recent revisions to earlier archaeological assumptions. Specific findings reveal mixtures of materials from different sources within individual sites, necessitating critical archaeological interpretation regarding the chrono-cultural significance of such assemblage heterogeneity. 

VORTEX is a robust tool that combines expanded datasets, advanced machine learning methods, and probabilistic uncertainty frameworks to aid in provenance research, while demonstrating its scalability and applicability to real-world archaeological datasets.