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This archaeometric study aims at characterizing the archaeological finds belonging to the lithic industry from La Calvera rock shelter (Camaleño, Cantabria) and at hypothesizing the possible provenance of each material. The site, located in the mountainous area of Picos de Europa National Park (more than 1000 m a.s.l.) close to the megalithic complex of Peña Oviedo, is characterized by the presence of hearths and charcoal remains, ancient pottery, and a rich lithic assemblage composed of siliceous rocks dating back to >8000 BP and linked to the first Holocene occupations of the Cantabrian Mountains. For the study of the rock shelter's lithic assemblage, a multi-analytical approach was used: SEM-EDS and XRD analyses were performed to define the microtextural characteristics of samples and to identify the amorphous/crystalline phases; physical and mechanical tests were conducted to define the petrophysical properties (density, porosity, imbibition and saturation indexes, mechanical strength) of the samples. Preliminary data of chemical compositions, obtained by portable XRF, are also presented. The results show that the different siliceous materials (quartzite, cherts, hyaline quartz) can be distinguished through the basic analytical techniques used here. In addition, most of the archaeological samples have mineralogical and petrographic features similar to the natural samples coming from nearby outcrops, corroborating the hypothesis of a local material supply. The presence of local sources of useful raw materials could have favoured the site's occupation. Finally, the diverse compositional and textural feature of the analysed materials result in different mechanical properties (porosity, density, hardness, workability), so they likely had different uses and technical functions. [ABSTRACT FROM AUTHOR]

Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification. [ABSTRACT FROM AUTHOR]