Synthesising microstructures of a partially frozen salty sand using voxel-based 3D generative adversarial networks

Generating synthetic material microstructures is essential in the numerical modelling of geomaterials. The occurrence of permafrost and saline groundwater overlapping regions is crucial in a series of phenomena, such as carbon emissions and subgrade settlements. The microstructure of geomaterials in these regions is of particular complexity because of the multiphase nature with salty water and ice crystals. This complexity renders existing generative models ineffective in synthesising their microstructures. Traditional generative methods are limited in the sense that require prior knowledge of material descriptors. Recently, machine learning generative models achieved unprecedented levels of performance and realism, but still lack the means to assess posterior error. This work aims to bridge the gap between traditional methods and deep learning generative models by assessing posterior image quality in the latter. A 3D Generative Adversarial Network (GAN) model is trained with image samples from an X-ray CT of a partially frozen salty sand. The metrics retained to assess posterior quality are particle fabric (shape parameter and anisotropy) and homogenised elastic coefficients obtained with Finite Element Method (FEM) simulations. A hyperparametric study on batch size and latent dimension serves to select the best configuration based on particle fabric. FEM simulations determine the deviation in the generated images elastic coefficients being 7.55% on average with respect to real samples. With 803 voxels, generated images are the largest up-to-date in a three-phase material, allowing to reach REV criteria. Applications range from the generation of microscales in double-scale models to the calibration of image processing tools.