  Fast multi-phase processes in methane hydrate-bearing samples are challenging
for micro-CT quantitative study because of complex tomographic data analysis
involving time-consuming segmentation procedures. This is due to the sample
multi-scale structure changing in time, low X-ray attenuation and phase
contrast between solid and fluid materials, as well as large amount of data
acquired during dynamic processes. We propose a hybrid approach for automatic
segmentation of tomographic data from time-resolved imaging of methane
gas-hydrate formation in sandy granular media. First, we use an optimized 3D
U-net neural network to perform segmentation of mineral grains that are
characterized by low contrast to the surrounding pore brine-saturated phases.
Then, we perform statistical clustering based on the Gaussian mixture model for
separating the pore-space phases that are characterized by gray-level
instabilities caused by dynamic processes during hydrate formation. The
proposed approach was used for segmenting several hundred gigabytes of data
acquired during an in-situ tomographic experiment at a synchrotron. Automatic
segmentation allowed for studying properties of the hydrate growth in pores, as
well as dynamic processes such as the incremental pore-brine flow and
redistribution.
