Algorithmic Provenance Estimation of Andean Metal Artifacts: A Predictive Framework Using Lead Isotope Ratios

This study employs machine learning methods to estimate possible source regions from Andean lead isotopes, a novel approach for tracing the provenance of metal artifacts in this region. Two Random Forest coordinate models were trained to approximate geographic provenance. The latitude model uses 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb isotope predictors, plus three derived ratios. The longitude model uses the same predictors together with the latitude predicted by the first model, allowing the regression to incorporate broad spatial coherence in the Andean ore-lead system. The Random Forest models reached, on validation, 3.50° and 1.94° MAE for latitude and longitude, respectively. These moderate values support the use of the framework as a screening tool that generates a ranked shortlist of nearest isotopic matches, rather than as a source of single definitive coordinates. Additionally, K-means clustering and Euclidean distance analysis were used to link artifact isotope compositions to known sources. The models’ limitations and scope were documented to ensure appropriate use and interpretation.