# WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation
###### Abstract  
Gravel soil faces significant engineering challenges such as leakage erosion and soil flow due to its complex composition and susceptibility to groundwater effects. This study integrates the entire machine learning process, including pre- and post-processing of images, WGAN implementation, and validation of hydraulic and morphological properties. Obtaining intact gravel soil samples is difficult and costly due to their erodible nature in the Li River, China. A \(\mu\)-CT scanning series is employed to capture detailed images with three microstructural characteristics of gravel soil, forming the basis for training datasets using WGANs. This approach allows the generation of similar 3D realizations that replicate the microstructural characteristics and hydraulic behaviors of a prototype of gravel soils. Through computational fluid dynamics (CFD) simulations, the effectiveness of the realizations in hydraulic behavior within reconstructed porous structures is verified. This process indirectly validates the consistency between the realization's microstructure and the prototype. This integrated methodology not only enhances understanding but also aids in the optimization of engineering designs and applications in geotechnical and materials science disciplines. WGANs (Wasserstein Generative Adversarial Networks); machine learning for porous structure; pore-scale CFD simulation; gravel soil; hydraulic properties imitation +
Footnote †: journal: Applied Sciences  
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geological scenarios utilizing the digital-twin technique for material microstructures [3; 4]. This method significantly reduces the need for extensive trial-and-error experiments. Generative adversarial networks (GANs), introduced by [PERSON] et al., 2014 [5], have become a prominent technique in the realm of reconstruction and digital-twin development using unsupervised intelligent deep learning approaches. GANs are notable for their capability to generate realistic data through the interplay of two neural networks: the generator and the discriminator, which compete against each other. This technology is valuable for tasks such as data generation, augmentation, and sample expansion and is particularly beneficial in scenarios with limited annotated data. Currently, GANs have found applications across various domains including soil moisture simulation [6], soil deformation and cracking simulation, and 3D digital core reconstruction of rock masses, as documented in recent studies [7]. These applications leverage GANs' ability to generate synthetic data that closely mimic real-world phenomena, aiding in modeling complex processes and enhancing understanding. To address challenges like unstable training dynamics inherent in GANs, [PERSON] et al. (2017) [8] introduced an advanced algorithm known as Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). This method helps mitigate issues such as gradient vanishing and exploding, thereby improving the stability and effectiveness of GAN training processes. In summary, GANs represent a powerful tool in modern data science and engineering, enabling the creation of synthetic data that enhance model training, simulation accuracy, and understanding of complex systems across diverse fields. The field of large-scale spatial model reconstruction, particularly in earth modeling applications such as reservoir characterization, often employs MPS (multiple-point statistics) methods. These techniques are essential for reconstructing stochastic random media in two- and three-dimensional scenarios. MPS methods excel in capturing the complex geological structures and patterns that typify the earth's subsurface, thereby addressing the heterogeneity and uncertainty inherent in such environments.