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Optimizing multiple non-invasive techniques (PXRF, pMS, IA) to characterize coarse-grained igneous rocks used as building stones

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We present a workflow to conduct a full characterization of medium to coarse-grained igneous rocks, using portable, non-invasive, and reproducible approaches. This includes: (i) Image Analysis (IA) to quantify mineral phase proportions, grain size distribution using the Weka trainable machine learning algorithm. (ii) Portable X-ray fluorescence spectrometer (PXRF, Bruker Tracer IV) to quantify the whole-rock's chemical composition. For this purpose, a specific calibration method dedicated to igneous rocks using the open-source CloudCal app was developed. It was then validated for several key elements (Si, Al, K, Ti, Ca, Fe, Mn, Sr, Ga, Ba, Rb, Zn, Nb, Zr, and Y) by analyzing certified standard reference igneous rocks. (iii) Portable Magnetic Susceptibilimeter (pMS, Bartington MS2K system) to constrain the mineralogical contribution of the samples. The operational conditions for these three methods were tested and optimized by analyzing five unprepared surfaces of igneous rocks ranging from a coarse-grained alkaline granite to a fine-grained porphyric diorite and hence, covering variable grain sizes, mineralogical contents, and whole-rock geochemical compositions. For pMS and PXRF tools, one hundred analyses were conducted as a 10 cm × 10 cm square grid on each sample. Bootstrap analysis was implemented to establish the best grid size sampling to reach an optimized reproducibility of the whole-rock signature. For PXRF analysis, averaged compositions were compared to PXRF analysis on press-pellets and laboratory WD-XRF analysis on fused disk and solution ICP-OES (for major) and solution-ICPMS (for trace element concentrations). Ultimately, this workflow was applied in the field on granitoids from three Roman quarrying sites in the Lavezzi archipelago (southern Corsica) and tested against the Bonifacio granitic War Memorial, for which its provenance is established. Our results confirm this information and open the door to geoarchaeological provenance studies with a high spatial resolution.
Tijdschrift: Journal of Archaeological Science
ISSN: 0305-4403
Volume: 129
Aantal pagina's: 20
Jaar van publicatie:2021
Trefwoorden:Geoarchaeology, Granite discrimination, PXRF, ML-Image segmentation, Magnetic susceptibility, Roman quarry