## 1 Introduction  
Digital core technology effectively overcomes the limitations of traditional rock experimental methods, including the difficulty of sample acquisition, equipment cost, and experimental period, by simulating the physical properties of rocks. This technology significantly reduces costs and improves experimental efficiency and the consistency of results. Compared to traditional methods like mercury intrusion and NMR [1], digital core technology offers significant advantages in evaluating microscopic pore structures. It uses digital images to reflect the internal structure of the core, precisely measuring key physical parameters such as porosity and permeability [2; 3]. It also facilitates the intuitive assessment of reservoir characteristics and connectivity and allows for an in-depth study of fluid flow mechanisms within rock pores [4; 5; 6]. Additionally, when combined with numerical simulation, this technology can create accurate reservoir models, predict pressure and production changes during extraction, and provide robust data support for reservoir management and optimized oil extraction strategies [7]. As a core component of the digital transformation in the petroleum industry, digital core technology is rapidly gaining widespread attention. In this field, image processing technology plays a crucial role. The integration of digital core technology with image processing has significantly enhanced our understanding of rock microstructures and markedly improved the accuracy of fluid flow simulations [8]. Using deep learning-based generative adversarial networks (GANs) like StyleGAN, it is possible to create realistic rock images [9], simulating various geological conditions. This extends the application scope of digital core technology and improves the accuracy of identifying rock's physical properties [10; 11]. Image segmentation technology is pivotal in this process [12], accurately identifying microscopic structures such as pores and fractures, thus providing a solid foundation for fluid flow simulation. The combination of these technologies enhances the value of digital core technology in geological exploration and resource development. This paper explores the integration of digital core technology and image processing from the perspectives of image generation and image segmentation. In the realm of image generation, GANs have demonstrated exceptional data-generation capabilities across various fields [13; 14]. For instance, in 2020, [PERSON] and [PERSON] improved the quality of a large-scale automotive image dataset using StyleGAN [15]. This advancement has paved new avenues for image data augmentation and quality improvement. StyleGAN2-ADA, an advanced version of StyleGAN, uses adaptive discriminator augmentation to generate high-resolution, visually realistic images. In 2023, [PERSON] et al. applied StyleGAN2-ADA to an algae classification model, significantly improving classification accuracy by 16% through image augmentation [16]. That same year, [PERSON] et al. generated high-resolution knee X-ray images using StyleGAN2-ADA, which closely resembled real images both visually and statistically, exhibiting characteristics akin to a real dataset [17]. In 2022, [PERSON] and [PERSON] ingeniously applied StyleGAN2-ADA to generate high-resolution 2D scanning electron microscope images, providing a new solution for the three-dimensional reconstruction of digital cores [18]. In 2023, [PERSON] et al. explored using StyleGAN for data augmentation to enhance the segmentation accuracy of medical CT images [19]. This study employs StyleGAN2-ADA technology to augment 2D CT grayscale images, aiming to improve image segmentation accuracy by enhancing image quality. The traditional methods of image segmentation technology are mainly based on two key characteristics: similarity and boundary discontinuity. Similarity means that the pixels in the same region of the image have similar gray values, which is the basis of the region segmentation method.