Abstract
Modern methods of lithological research allow obtaining results in digital form. This increases the objectivity, accuracy, reliability of data on rocks and makes possible their further computer processing and modeling. The article studies the issue of analyzing the composition and properties of rocks using digital photographs of the core sample. Existing techniques shall be considered. A scheme for the automated assessment of porosity and fracture parameters based on photographs of petrographic thin sections shall be proposed: 1) semi-automatic selection of voids by color; 2) automatic selection of cracks based on geometric features; 3) estimation of parameters of absolute porosity, total length and average width of cracks. To highlight voids, color filtering was applied with interactive input of the values of the classification attributes. Allocation of cracks from the general mass of voids was carried out according to the characteristics of the shape and area. The value of the absolute porosity was estimated as the ratio of the area of the selected voids to the total area of the photography. To determine the average width and total length of the cracks, the inscribed rectangle approximation was used. The scheme is implemented using algorithms of the OpenCV library, integrated into a custom software application. Convergence of the results of using the application with the results of a non-automated expert assessment for the parameter of porosity 80%, fracturing 70%. Estimation errors refer to cases of low color contrast of voids and mineral skeleton and segmentation of fracture lines in the original photographs. When using the application, a significant increase in speed and a decrease in the labor intensity of a specialist’s work was obtained. This allows us to recommend the developed application for express analysis of photographs of core thin sections.
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Prozorova, G., Slinkina, E. (2023). Assessment of Porosity and Fracturing of Rocks Using Digital Photographs of Core Thin Sections. In: Guda, A. (eds) Networked Control Systems for Connected and Automated Vehicles. NN 2022. Lecture Notes in Networks and Systems, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-031-11051-1_163
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DOI: https://doi.org/10.1007/978-3-031-11051-1_163
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