Image segmentation is a crucial step of almost any Digital Rock workflow. In this paper, we propose an approach for generation of a labelled dataset and investigate an application of three popular convolutional neural networks (CNN) architectures for segmentation of 3D microtomographic images of samples of various rocks. Our dataset contains eight pairs of images of five specimens of sand and sandstones. For each sample, we obtain a single set of microtomographic shadow projections, but run reconstruction twice: one regular high-quality reconstruction, and one using just a quarter of all available shadow projections. Thoughtful manual Indicator Kriging (IK) segmentation of the full-quality image is used as the ground truth for segmentation of images with reduced quality. We assess the generalization capability of CNN by splitting our dataset into training and validation sets by five different manners. In addition, we compare neural networks results with segmentation by IK and thresholding. Segmentation outcomes by 2D and 3D U-nets are comparable to IK, but the deep neural networks operate in automatic mode, and there is big room for improvements in solutions based on CNN. The main difficulties are associated with the segmentation of fine structures that are relatively uncommon in our dataset.
Excited-state absorption (ESA) in various bismuth-doped fibers (BDFs) was investigated. No significant ESA in IR emission bands of Bi-doped germanosilicate and phosphosilicate fibers was found. Considerable ESA was observed in Bi-doped aluminosilicate fibers at 800-1700 nm. The ESA spectra of the aluminosilicate BDFs with different bismuth concentration were measured at room and 77 K temperature. Significant dependence of the ESA on the bismuth concentration and the fiber temperature was found.
The aim of the work is the study of structural and textural parameters of sedimentary rocks with application of wide complex of modern technical instruments including mathematical and software methods for data acquisition and processing. We have considered the capabilities of investigation of mineral content and its spatial distributions in thin section by using of two imaging techniques with different resolution and nature of registered signal. It confirmed the need for complementary study of the data of various physical methods.
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