Abstract Determining the provenance of archeological material is essential for documenting the movement of ancient resources and placing fossils and artifacts in the correct stratigraphic context. Non-destructive geochemical compositional analysis… Click to show full abstract
Abstract Determining the provenance of archeological material is essential for documenting the movement of ancient resources and placing fossils and artifacts in the correct stratigraphic context. Non-destructive geochemical compositional analysis with Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometry has the potential to provide a versatile approach to studying the provenance of archeological material. It is possible to build large regional XRF datasets that are useful for understanding the movements of toolmakers because this technique can be applied to a large number of samples non-destructively. However, incorporating new provenance data into existing datasets poses several potential challenges. Expanded provenance datasets often require the comparison of geochemical data across XRF instruments and settings. In particular the addition of different lithologies requires an understanding of diverse degrees of surface weathering and matrix heterogeneity. On the Homa Peninsula in Kenya, Oldowan tools from Kanjera South were previously linked to primary and secondary sources based on trace element geochemistry determined through ED-XRF. In this study, we established comparability across two ED-XRF instruments and incorporated samples from additional sources of quartzite and rhyolite into the existing raw material sourcing dataset. The new sources revealed regional differences in the geochemistry of rhyolite and quartzite that derive from drainages on and around the Homa Peninsula. This demonstrates the compatibility of quartzite and rhyolite materials with non-destructive ED-XRF sourcing and provides a methodological establishment of cross-study comparison in elemental values derived from non-uniform whole rock samples. This study highlights the importance of calibration using overlapping reference standards and linear regressions to determine offsets necessary for direct comparison.
               
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