Morphometric Analysis through 3D Modelling of Bronze Age Stone Moulds from Central Sardinia

Stone moulds were basic elements of metallurgy during the Bronze Age, and their analysis and characterization are very important to improve the knowledge on these artefacts useful for typological characterization. The stone moulds investigated in this study were found during an archaeological field survey in several Nuragic (Bronze Age) settlements in Central Sardinia. Recent studies have shown that photogrammetry can be effectively used for the 3D reconstruction of small and medium-sized archaeological finds, although there are still many challenges in producing high-quality digital replicas of ancient artefacts due to their surface complexity and consistency. In this paper, we propose a multidisciplinary approach using mineralogical (X-ray powder diffraction) and petrographic (thin section) analysis of stone materials, as well as an experimental photogrammetric method for 3D reconstruction from multi-view images performed with recent software based on the CMPMVS algorithm. The photogrammetric image dataset was carried out using an experimental rig equipped with a 26.2 Mpix full frame digital camera. We also assessed the accuracy of the reconstruction models in order to verify their precision and readability according to archaeological goals. This allowed us to provide an effective tool for more detailed study of the geometric-dimensional aspects of the moulds. Furthermore, this paper demonstrates the potentialities of an integrated minero-petrographic and photogrammetric approach for the characterization of small artefacts, providing an effective tool for more in-depth investigation of future typological comparisons and provenance studies.

Pretraining Convolutional Neural Networks for Mudstone Petrographic Thin-Section Image Classification

Convolutional neural networks (CNN) are currently the most widely used tool for the classification of images, especially if such images have large within- and small between- group variance. Thus, one of the main factors driving the development of CNN models is the creation of large, labelled computer vision datasets, some containing millions of images. Thanks to transfer learning, a technique that modifies a model trained on a primary task to execute a secondary task, the adaptation of CNN models trained on such large datasets has rapidly gained popularity in many fields of science, geosciences included. However, the trade-off between two main components of the transfer learning methodology for geoscience images is still unclear: the difference between the datasets used in the primary and secondary tasks; and the amount of available data for the primary task itself. We evaluate the performance of CNN models pretrained with different types of image datasets—specifically, dermatology, histology, and raw food—that are fine-tuned to the task of petrographic thin-section image classification. Results show that CNN models pretrained on ImageNet achieve higher accuracy due to the larger number of samples, as well as a larger variability in the samples in ImageNet compared to the other datasets evaluated.

An ecophysiological explanation for manganese enrichment in rock varnish

Desert varnish is a dark rock coating that forms in arid environments worldwide. It is highly and selectively enriched in manganese, the mechanism for which has been a long-standing geological mystery. We collected varnish samples from diverse sites across the western United States, examined them in petrographic thin section using microscale chemical imaging techniques, and investigated the associated microbial communities using 16S amplicon and shotgun metagenomic DNA sequencing. Our analyses described a material governed by sunlight, water, and manganese redox cycling that hosts an unusually aerobic microbial ecosystem characterized by a remarkable abundance of photosynthetic Cyanobacteria in the genus Chroococcidiopsis as the major autotrophic constituent. We then showed that diverse Cyanobacteria, including the relevant Chroococcidiopsis taxon, accumulate extraordinary amounts of intracellular manganese—over two orders of magnitude higher manganese content than other cells. The speciation of this manganese determined by advanced paramagnetic resonance techniques suggested that the Cyanobacteria use it as a catalytic antioxidant—a valuable adaptation for coping with the substantial oxidative stress present in this environment. Taken together, these results indicated that the manganese enrichment in varnish is related to its specific uptake and use by likely founding members of varnish microbial communities.

Labyrinth patterns in Magadi (Kenya) cherts: Evidence for early formation from siliceous gels

Sedimentary cherts, with well-preserved microfossils, are known from the Archean to the present, yet their origins remain poorly understood. Lake Magadi, Kenya, has been used as a modern analog system for understanding the origins of nonbiogenic chert. We present evidence for synsedimentary formation of Magadi cherts directly from siliceous gels. Petrographic thin-section analysis and field-emission scanning electron microscopy of cherts from cores drilled in Lake Magadi during the Hominin Sites and Paleolakes Drilling Project in 2014 led to the discovery of two-dimensional branching "labyrinth patterns" in chert, which are a type of fractal "squeeze" pattern formed at air-liquid interfaces. Labyrinth patterns preserved in chert from Lake Magadi cores indicate invasion of air along planes in dewatering gels. These patterns support the precipitation of silica gels in the saline-alkaline Lake Magadi system and syndepositional drying of gels in contact with air as part of chert formation. Recognizing cherts as syndepositional has been critical for our use of them for U-Th dating. Identification of labyrinth patterns in ancient cherts can provide a better understanding of paleoenvironmental and geochemical conditions in the past.

SIMS analyses of the oldest known assemblage of microfossils document their taxon-correlated carbon isotope compositions

Analyses by secondary ion mass spectroscopy (SIMS) of 11 specimens of five taxa of prokaryotic filamentous kerogenous cellular microfossils permineralized in a petrographic thin section of the ∼3,465 Ma Apex chert of northwestern Western Australia, prepared from the same rock sample from which this earliest known assemblage of cellular fossils was described more than two decades ago, show their δ13C compositions to vary systematically taxon to taxon from −31‰ to −39‰. These morphospecies-correlated carbon isotope compositions confirm the biogenicity of the Apex fossils and validate their morphology-based taxonomic assignments. Perhaps most significantly, the δ13C values of each of the five taxa are lower than those of bulk samples of Apex kerogen (−27‰), those of SIMS-measured fossil-associated dispersed particulate kerogen (−27.6‰), and those typical of modern prokaryotic phototrophs (−25 ± 10‰). The SIMS data for the two highest δ13C Apex taxa are consistent with those of extant phototrophic bacteria; those for a somewhat lower δ13C taxon, with nonbacterial methane-producing Archaea; and those for the two lowest δ13C taxa, with methane-metabolizing γ-proteobacteria. Although the existence of both methanogens and methanotrophs has been inferred from bulk analyses of the carbon isotopic compositions of pre-2,500 Ma kerogens, these in situ SIMS analyses of individual microfossils present data interpretable as evidencing the cellular preservation of such microorganisms and are consistent with the near-basal position of the Archaea in rRNA phylogenies.