# WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation
###### Abstract
Historically, methods like simulated annealing have been pivotal in stochastic modeling for their ability to incorporate arbitrary cost functions related to statistical and morphological properties, which are crucial for unconditional three-dimensional image reconstruction. These techniques laid the groundwork for more advanced approaches that followed. In recent years, there has been significant innovation with the integration of MPS methods and GANs (Generative Adversarial Networks). [PERSON] (2018) [9] proposed a method that combines MPS and GANs for geological modeling, specifically for generating features like river channels and reservoirs. This integration leverages GANs' ability to produce realistic and diverse synthetic data, enhancing the fidelity and variability of geological models beyond what traditional MPS methods alone can achieve. This combined approach allows the creation of highly detailed and varied geological structures, improving the accuracy and usefulness of earth modeling in applications ranging from hydrology to oil and gas reservoir characterization. The synergy between MPS and GANs represents a promising direction in spatial modeling, enabling more robust simulations and predictions in geosciences. In reconstructing small- or pore-scale media, researchers utilize high-resolution digital images from techniques like \(\mu\)-CT or medical CT scans. Generative Adversarial Networks (GANs) are then employed to generate virtual samples mimicking these structural characteristics. To enhance fidelity, GANs employ gradient-differentiable Wasserstein distance as a loss function, surpassing limitations of traditional divergences like KL and JS. Additionally, penalty functions ensure stable training, addressing issues such as mode collapse and improving overall sample quality. This approach facilitates accurate reconstruction and modeling of complex materials in fields such as soil science and material science. This paper introduces a systematic approach to digitally reconstructing gravel soil samples using \(\mu\)-CT scan slice images and advanced computational techniques, involving image processing, MATLAB (R2022a) coding, PyTorch (3.9) programming, working flow, and so on. In Section 2, a dataset is meticulously constructed from these \(\mu\)-CT scan images, involving the acquisition, preprocessing, and compilation of data suitable for machine learning. In Section 3, a generator model based on WGAN-GP is trained with the dataset to meticulously recreate digital core samples, placing strong emphasis on fidelity assessment and evaluating model performance. In Section 4, computational fluid dynamics (CFD) simulations are employed to validate the consistency of the hydraulic properties of these generated models in comparison to the training set (prototype). By simulating fluid behavior within the reconstructed porous structures, engineers can gain insights into how these materials perform under different flow conditions, informing design decisions and optimizing applications in fields such as materials science and engineering. In Section 5, the main hydraulic properties of soils and some statistical functions are then employed to quantitatively measure the similarity between the reconstructed samples and the original data, ensuring robustness and accuracy in the reconstruction process. Section 6 includes a discussion of the processes and indices, such as specific area and Eula characteristics, that facilitate the assessment of hydraulic properties and validate the effectiveness of the generation method for practical engineering applications. Some conclusions are drawn in Section 7. This comprehensive approach underscores the integration of imaging, machine learning, and simulation techniques to advance understanding and analysis in geological and engineering studies.