## 3 Research Framework
### Feature Extraction Based on Deep-Learning Model  
In recent years, the application of 3D laser scanning technology in mine stability analysis has become increasingly widespread, offering new possibilities for the precise modeling of geological and engineering problems through the generation of high-resolution point cloud data [34]. As early as 2013, [PERSON] et al. conducted a comparative study based on a collected LiDAR-scanned rock mass database, highlighting the advantages of 3D laser imaging in the analysis of blocky rock mass tunnel stability [35]. [PERSON] et al. explored the use of 3D laser scanners for monitoring deformations in mining \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lithology** & **Density** & **Tensile** & **Tensile** & **Cohesive** & **Angle of Internal** & **Poisson Ratio** \\  & **(g/cm\({}^{3}\))** & **Strength/MPa** & **Strength/GPa** & **/MPa** & \(I^{\circ}\) & \\ \hline \hline siltstone & 2.62 & 0.38 & 3.55 & 0.33 & 33.81 & 0.24 \\ dolomite & 2.79 & 0.90 & 6.87 & 0.80 & 35.65 & 0.19 \\ limestone & 2.64 & 0.86 & 6.76 & 0.77 & 37.03 & 0.19 \\ ore body & 2.84 & 1.11 & 10.33 & 1.08 & 35.97 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Macro-physical and mechanical parameters of the ore body of the Duimenshan mine section. Figure 3: Field stress distribution. This figure illustrates the distribution of zone maximum principal stress obtained through numerical simulations of the Baiyang mining area (195 slope) and the Dumenshan mining area (167 slope) using FLAC3D software. In structural analysis, principal stress refers to the stress experienced in a specific direction and serves as a critical indicator for assessing potential material failure. The key parameters for the calculation are set as follows: cut plane: on; calculated by: volumetric averaging. In the figure, the color of each zone represents different value ranges, increasing from blue to red. Specific values can be referenced using the color scale bar on the right. The areas marked by the three red circles are the main focus of this study. areas, emphasizing the importance of high-resolution data in identifying and predicting geological deformations [36]. These studies suggest that laser-scanned images contain valuable latent information, which can contribute to a more comprehensive prediction and evaluation of mine stability. That is to say, the idea of using a deep-learning model to extract image information in this study is applicable and feasible. Unfortunately, previous stress prediction efforts have not capitalized on this growing research resource. Therefore, for each mined-out area, we incorporate the three-view laser-scanned images as a research resource, as shown in Figure 4.  
We follow the current mainstream practice of extracting image features using deep-learning models and take a two-step approach to perform the feature extraction: Firstly, the initial high-dimensional features of the image are obtained using a pre-trained deep-learning model, followed by a dimensionality reduction algorithm to obtain the usable final features. The reason for performing a dimensionality reduction algorithm is that the initial high-dimensional features are likely to create a dimensionality disaster for subsequent machine-learning algorithms [37]. In the first step, we use a variety of deep-learning models, including ResNet-50 [38], DenseNet-121 [39], Inception-v4 [40], Xception [41], and the Shifted Window Transformer (Swin Transformer) [42]. These models have demonstrated excellent performance in various computer vision tasks and exhibit strong feature extraction capabilities.