Super-resolution reconstruction of hydrate-bearing CT images for microscopic detection of pore

Abstract The pore structure of marine natural gas-hydrate-bearing sediments is a key factor related to the physical properties of reservoirs. However, the resolution of micro-computed tomography (micro-CT) images is unsuitable for the analysis of pore structures in fine-grained sediments. In this regard, super-resolution (SR) reconstruction technology is expected to improve the spatial resolution of micro-CT images. We present a self-supervised learning method that does not require high-resolution datasets as input images to complete the training and reconstruction processes. This method is an end-to-end network consisting of two subnetworks: an SR network and a downscaling network. We trained on a self-built dataset of hydrate samples from three different particle sizes. Compared with typical methods, the SR results indicate that our method provides high resolution while improving clarity. In addition, it has the highest consistency with the liquid saturation method with the subsequent calculation of porosity parameters. This study contributes to the investigation of seepage and energy transfer in sediments containing natural gas hydrates, which is particularly important for the exploration and development of marine natural gas hydrate resources.