VORTEX (Variscite origin recognition technology X-ray based) Data. A European geoarchaeological green phosphate...

Area of Science:

• Geoarchaeology
• Archaeological Science
• Materials Science

Background:

• Prehistoric mining of phosphate minerals in Iberia is well-documented.
• Understanding the provenance of archaeological artifacts is crucial for reconstructing past social networks and trade routes.
• Existing methods for artifact provenance can be destructive or lack precision.

Purpose of the Study:

• To develop and validate a data-driven framework for determining the provenance of archaeological artifacts using elemental composition.
• To integrate portable X-ray fluorescence (p-XRF) analysis with machine learning (ML) for artifact sourcing.
• To create a comprehensive dataset of green phosphate mineral compositions from Iberian deposits.

Main Methods:

• Compositional analysis of 1778 geoarchaeological samples using portable X-ray fluorescence (p-XRF).
• Mineralogical analysis of a subset of samples (N=249) using X-ray diffraction (XRD).
• Development and application of a machine learning model trained on elemental composition data to predict artifact provenance.

Main Results:

• A robust framework integrating p-XRF and ML was established for accurate artifact provenance determination.
• Elemental composition data (atomic percentages of Al-Th) and mineralogical data were systematically collected and presented.
• The model successfully traced the origin of 571 variscite beads from various archaeological sites.

Conclusions:

• The developed framework offers a non-destructive and accurate method for sourcing archaeological artifacts.
• This dataset and methodology have significant potential for future research in artifact provenance and social network analysis.
• The study provides valuable geochemical and mineralogical data for Iberian green phosphate deposits.