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.
Located in
Library
/
RBINS Staff Publications 2021
The empirical power law relation (PR) between resonance frequency (f0), obtained from H/V spectral ratio analysis of ambient noise, and sediment thickness (h), obtained from boreholes, is frequently used in microzonation studies to predict bedrock depth. In this study, we demonstrate (i) how to optimally construct a PR by including the error on the picked f0 in the regression, and (ii) how to evaluate a regression quality by identifying the under- or overestimation of the sediment thickness prediction. We apply this methodology on f0 data derived from 74 ambient noise recordings acquired above boreholes that reach the Brabant Massif bedrock below Brussels (Belgium). Separating the f0 data into different subset based on the cover geology does not significantly improve the bedrock depth prediction because the cover geology in Brussels has common base layers. In Brussels, the PR relation h = 88.631.f0−1.683 is the best candidate to convert f0 to depth, with a prediction error of 10%. The Brussels PR was subsequently applied on a local survey (404 measurements; 25 km2) in southern Brussels with the aim to study Brussels’ Brabant Massif bedrock paleorelief. By linking the obtained paleorelief, Bouguer gravity data and aeromagnetic data, a NNW-SSE oriented, 20 m-high subsurface ridge could be identified. This ridge stands out because of differential erosion between less-resistant and hard quartzitic rock formations of the Brabant Massif. This subsurface ridge deflects the local radiation of seismic energy resulting in an anomaly in the otherwise regional consistent azimuthal dependency of the resonance frequency. We conclude that adding a polarisation analysis to a microzonation survey analysis allows detecting anomalous features in the paleorelief.
Located in
Library
/
RBINS Staff Publications 2021
