Super-resolution reconstruction of hydrate-bearing sediment computed
tomography images for microscopic detection of pore structure
Abstract
The pore structure of marine sediments varies with the distribution of
gas-hydrate, hence affecting the gas-water permeability. CT image is a
conventional approach to view the internal structure, while for
hydrate-bearing sediment investigation, rather poor resolution of
obtained image has limited the accuracy of the analysis. Recently,
super-resolution (SR) reconstruction techniques have been used to
enhance the spatial resolution of CT images with varying degrees of
improvement. Typical Image Pairs-Based SR (PSR) methods require higher
resolution matching images for training, which is challenging for
hydrate samples in dynamic temperature and pressure conditions. Here, we
introduced a self-supervised learning (SLSR) method that only relies on
a single input image to complete the process of training and
reconstruction. We conducted a complete training to establish an
end-to-end network consisting of two sub-networks, an SR network and a
downscaling network. Self-built datasets from three hydrate samples with
different sediment grains were trained and tested. Compared with the
typical method, the SR results show that our method provides higher
resolution while improving clarity. Moreover, in the subsequent
calculation of porosity parameters, it has the highest consistency with
the liquid saturation method. This study contributes to investigating
the water seepage and energy transfer in the gas hydrate bearing
sediments, which is particularly important for the exploration and
development of marine natural gas hydrate resources. The image
super-resolution method established by us has also a broad application
prospect in the field of CT imaging.