Statistical Learning Improves Classification of Limestone Provenance

Determining the lithostratigraphic provenance of limestone artefacts is challenging. We addressed the issue by analysing Roman stone artefacts, where previously traditionalpetrological methods failed to identify the provenance of 72% of the products due to the predominance of micrite limestone. We applied statistical classification methods to 15 artefacts using linear discriminant analysis, decision trees, random forest, and support vector machines. The latter achieved the highest accuracy, with 73% of the samples classified to the same stratigraphic member as determined by the expert. We improved classification reliability and evaluated it by aggregating the results of different classifiers for each stone product. Combining aggregated results with additional evidence from paleontological data or precise optical microscopy leads to successful provenance determination. After a few samples were reassigned in this procedure, a support vector machine correctly classified 87% of the samples. Strontium isotope ratios (87Sr/86Sr) proved particularly effective as provenance indicators. We successfully assigned all stone products to local sources across four lithostratigraphic members, thereby confirming local patterns of stone use by Romans. We provide guidance for future use of statistical learning in provenance determination. Our integrated approach, combining geological and statistical expertise, provides a robust framework for challenging provenance determination.