Evaluation of pore-fracture microstructure of gypsum rock fragments using micro-CT

AbstraktAbstract

This study utilized X-ray micro-computed tomography (micro-CT) to investigate weathered gypsum rocks which can or do serve as a rock substrate for endolithic organisms, focusing on their internal pore-fracture microstructure, estimating porosity, and quantitative comparison between various samples. Examining sections and reconstructed 3D models provides a more detailed insight into the overall structural conditions within rock fragments and the interconnectivity in pore networks, surpassing the limitations of analyzing individual 2D images. Results revealed diverse gypsum forms, cavities, fractures, and secondary features influenced by weathering. Using deep learning segmentation based on the U-Net models within the Dragonfly software enabled to identify and visualize the porous systems and determinate void space which was used to calculate porosity. This approach allowed to describe what type of microstructures and cavities is responsible for the porous spaces in different gypsum samples. A set of quantitative analysis of the detected void and modeled networks provided a needed information about the development of the pore system, connectivity, and pore size distribution. Comparison with mercury intrusion porosimetry showed that both methods consider different populations of pores. In our case, micro-CT typically detects larger pores (> 10 μm) which is related to the effective resolution of the scanned images. Still, micro-CT demonstrated to be an efficient tool in examining the internal microstructures of weathered gypsum rocks, with promising implications particularly in geobiology and microbiology for the characterization of lithic habitats.

This study utilized X-ray micro-computed tomography (micro-CT) to investigate weathered gypsum rocks which can or do serve as a rock substrate for endolithic organisms, focusing on their internal pore-fracture microstructure, estimating porosity, and quantitative comparison between various samples. Examining sections and reconstructed 3D models provides a more detailed insight into the overall structural conditions within rock fragments and the interconnectivity in pore networks, surpassing the limitations of analyzing individual 2D images. Results revealed diverse gypsum forms, cavities, fractures, and secondary features influenced by weathering. Using deep learning segmentation based on the U-Net models within the Dragonfly software enabled to identify and visualize the porous systems and determinate void space which was used to calculate porosity. This approach allowed to describe what type of microstructures and cavities is responsible for the porous spaces in different gypsum samples. A set of quantitative analysis of the detected void and modeled networks provided a needed information about the development of the pore system, connectivity, and pore size distribution. Comparison with mercury intrusion porosimetry showed that both methods consider different populations of pores. In our case, micro-CT typically detects larger pores (> 10 μm) which is related to the effective resolution of the scanned images. Still, micro-CT demonstrated to be an efficient tool in examining the internal microstructures of weathered gypsum rocks, with promising implications particularly in geobiology and microbiology for the characterization of lithic habitats.