## 3 Results and Discussion
### Image Segmentation Results
#### 3.3.4 Three-Dimensional Pore Reconstruction and Micro-Pore Structure Analysis  
To further evaluate the segmentation model's performance, this study segmented 840 consecutive 2D CT grayscale images of the first scanned sandstone column. Subsequently, all the segmentation results were reassembled into a 3D pore map in their original sequence and compared with the real 3D pore map of the first sandstone column (Figure 9a,b). Previously, observing the segmentation results from a 2D perspective allowed only the single-image analysis of each segmentation map's shortcomings. However, synthesizing the 2D segmentation results into a 3D image can more comprehensively reveal the segmentation model's deficiencies. This 3D comparative analysis method not only showcases the local errors of each segmentation image but also reflects the cumulative effect of these errors in the overall structure, providing a more comprehensive and in-depth basis for optimizing the segmentation model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **IoU** & **PA** & **Porosity Difference** \\ \hline SegNet & 0.9538 & 0.9567 & 0.0968 \\ ResNet+U-Net & 0.9710 & 0.9880 & 0.0552 \\ U-Net++ & 0.9806 & 0.9699 & 0.0313 \\ DAN & 0.9851 & 0.9712 & 0.0259 \\ EM & 0.9866 & 0.9794 & 0.0116 \\ MN & 0.9880 & 0.9824 & 0.0079 \\ Ours & 0.9981 & 0.9993 & 0.0035 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different segmentation methods. As shown in Figure 10, the distribution of throat lengths in both the real and synthetic 3D pores exhibits a certain degree of consistency, despite some subtle differences. Prior to the intersection point, the synthetic 3D pores show a higher probability distribution in the shorter throat length region, indicating that the segmentation model is more sensitive to, or more inclined towards, identifying shorter throat structures. However, beyond the intersection point, the probability distribution of the real 3D pores surpasses that of the synthetic ones, suggesting that the segmentation model's accuracy in recognizing longer throats requires improvement. This variation implies that the model may need further optimization to more accurately reflect the throat length characteristics of real sandstone. Despite these differences, the overall distribution trends in both datasets are similar, indicating that the synthetic 3D pores can fairly approximate the throat length distribution of real sandstone to a certain extent. This similarity reflects the segmentation model's high accuracy in simulating the pore structure of sandstone. Nevertheless, to more precisely emulate the characteristics of real sandstone, especially in recognizing longer throats, the segmentation model may need further fine-tuning to balance the identification of the throats of varying lengths. As depicted in Figure 10, analyzing the coordination number distribution of the real and synthetic 3D pores reveals a similar high probability distribution in the low coordination number region, indicating that most pore points have few connections. This similarity suggests that both the real and synthetic pores predominantly feature a pore network with low coordination numbers. However, as the coordination number increases, the probability distribution of the synthetic pores diverges from that of the real pores. Specifically, the probability distribution of synthetic pores drops rapidly when the coordination number exceeds three, whereas the probability distribution of the real pores declines more gradually. This discrepancy may reveal limitations or biases in the segmentation model's ability to identify high coordination number pore structures. Particularly in regions with a coordination number greater than four, the probability distribution of the synthetic pores is significantly lower than that of real pores, potentially impacting the accurate representation of the complexity of the sandstone pore network.