## 3 Results and Discussion
### Limitations Analysis and Future Prospects  
In the task of pore segmentation in the 2D CT grayscale images of sandstone, the segmentation model exhibited outstanding performance. However, when applied to 3D data, its performance significantly declined, particularly in identifying medium-radius pores. This decline in performance stems from the model's overfitting in this specific area, leading to increased sensitivity to noise and a failure to adequately learn and simulate the complex morphological characteristics of medium-radius pores. Additionally, this study focused solely on sandstone, without encompassing other rock types such as carbonate rocks and shale, which limits the model's generalizability. Considering the differences in pore structures across different rock types, the model's performance may decline when applied to new rock types. The study foresees challenges in generalizing the model's capabilities to identify pores in a diverse array of rock types. Continuing  
Figure 11: (**a**) Pore radius distribution of real and synthetic 3D pores; (**b**) throat shape factor distribution of real and synthetic 3D pores.  
to use the StyleGAN2-ADA generative model may not suffice to meet this challenge, as the model's stability and training time will be affected by the increase in rock types. Furthermore, the current semi-supervised image segmentation method combining Adaptive Bagging, pseudo-labeling, and U-Net shows limitations when dealing with a growing variety of rock types. Particularly, the computational complexity of Adaptive Bagging may increase sharply, necessitating a reevaluation of the ensemble model's number and structure. However, the adaptive weight adjustment mechanism will demonstrate its advantages in handling more complex tasks. The pseudo-labeling strategy introduces a potential issue: mixing less accurate labels with professionally annotated data could lead to decreased accuracy during model training. Although our proposed method of calculating porosity consistency scores alleviates this problem to some extent, its effectiveness may be limited as the variety of rock types increases. Therefore, developing more refined algorithms to optimize this process is necessary for the future.  
The study has the potential for further improvement, particularly in generalizing across different rock types, and plans to expand the method to accommodate a broader range of rock types to enhance its generalizability. Additionally, the authors are committed to making a series of innovative adjustments to the model. First, they will attempt to combine the Adaptive Bagging ensemble learning method with the StyleGAN2-ADA model. This innovative combination is expected to significantly enhance the model's performance in handling complex tasks. The plan involves integrating multiple StyleGAN2-ADA models trained on different rock types or pore features to enhance the model's adaptability to diverse rock types. Second, for optimizing the segmentation method, the traditional ensemble U-Net model may be insufficient to handle increasing complexity. Therefore, the study will draw on the methods proposed in the DatasetGAN model [48], training a Style Interpreter network with a small amount of labeled data. This network will serve as a label generation branch within the StyleGAN2-ADA architecture, automatically generating high-quality pixel-level annotations for many unlabeled images. By combining the advantages of ensemble learning strategies with the StyleGAN2-ADA model, we expect to significantly improve the model's generation and segmentation capabilities when handling large-scale and highly complex tasks. These strategies will significantly enhance the model's performance while providing new perspectives and solutions for the field of rock pore segmentation.