Quantifying microstructures of earth materials using higher-order spatial correlations and deep generative adversarial networks.

ABSTRACT: The key to most subsurface processes is to determine how structural and topological features at small length scales, i.e., the microstructure, control the effective and macroscopic properties of earth materials. Recent progress in imaging technology has enabled us to visualise and characterise microstructures at different length scales and dimensions. However, one limitation of these technologies is the trade-off between resolution and sample size (or representativeness). A promising approach to this problem is image reconstruction which aims to generate statistically equivalent microstructures but at a larger scale and/or additional dimension. In this work, a stochastic method and three generative adversarial networks (GANs), namely deep convolutional GAN (DCGAN), Wasserstein GAN with gra

SUBMITTER: Amiri H 

PROVIDER: S-EPMC9889385 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature