A novel method to visualize liquid distribution in snow: superimposition of MRI and X-ray CT images

• Published in: Annals of Glaciology
• Main Authors: Yamaguchi, Satoru, Adachi, Satoru, Sunako, Sojiro
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press (CUP) 2023
• Subjects: Annals of Glaciology
• Online Access: http://dx.doi.org/10.1017/aog.2023.77; https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305523000770

Abstract The relationship between the behavior of water in snow and its microstructure is crucial to improve the prediction of wet snow disasters. X-ray computed tomography (X-ray CT) is frequently used to observe snow microscopically. However, distinguishing between ice and water in the X-ray images is difficult because ice exhibits an X-ray absorption coefficient similar to that of water. In contrast, magnetic resonance imaging (MRI) acquires nuclear magnetic resonance (NMR) signals of protons in a liquid and visualizes the NMR signal intensity, enabling discrimination between water and ice signals. However, snow grains and pore spaces cannot be distinguished in MRI images because they do not generate NMR signals. To investigate the relationship between the microstructure of snow and the distribution of liquids in snow, we developed a novel method that combines X-ray CT and MRI images to compensate for the disadvantages associated with each method. Using this method, we successfully visualized where liquid (C 12 H 24 ) occupied pore spaces. We also showed the possibility of using C 12 H 24 instead of water to obtain water retention curve of snow cover, which is a fundamental aspect of hydraulic properties. Although there is room for improvement in the visualization of water in snow, such as shortening the imaging time to escape snow metamorphosis and image superimposition methods, this method is expected to effectively elucidate the behavior of water in snow and clarify the characteristics of wet snow.