Quantitative analysis of dynamic CT imaging of methane-hydrate formation with a hybrid machine learning approach

TL;DR

This paper introduces a hybrid machine learning method combining neural networks and statistical clustering to automatically segment and analyze dynamic micro-CT images of methane hydrate formation, enabling detailed pore-scale studies.

Contribution

It presents a novel hybrid approach that automates segmentation of complex, large-scale tomographic data during hydrate formation, improving analysis efficiency and accuracy.

Findings

Successful segmentation of large datasets during hydrate formation

Enhanced understanding of hydrate growth and pore dynamics

Automated analysis reduces manual segmentation effort

Abstract

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.