## 4 Results
### Lithology Classification Feature Extraction  
(1) Dimension reduction  
To facilitate data visualization and reduce computational complexity, this study reduces the dimensionality of high-dimensional data to two dimensions. The low-dimensional embeddings are then visualized to assess the effectiveness of different dimensionality reduction techniques. Figure 8 presents the two-dimensional projection results of the training samples using PCA, t-SNE, and UMAP. It is evident that UMAP yields markedly different results compared with the other two methods. PCA, a linear dimensionality reduction technique, produces a projection in which lithological classes appear as overlapping clusters distributed along linear directions in the 2D space. In contrast, both t-SNE and UMAP capture nonlinear relationships, resulting in more dispersed and irregular cluster distributions. However, t-Distributed Stochastic Neighbor Embedding (t-SNE) has notable limitations: it is memory-intensive and poorly suited for large datasets. Moreover, t-SNE fails to preserve the global structure of the data--only intra-cluster distances are meaningful, while inter-cluster relationships are distorted and unreliable. Principal Component Analysis (PCA) also has constraints, primarily its assumption of Gaussian-distributed data, which reduces its effectiveness for complex geological datasets. In comparison, Uniform Manifold Approximation and Projection (UMAP) is better suited for handling large non-Gaussian datasets. UMAP not only preserves local neighborhood relationships but also maintains the global structure of the data more effectively, ensuring the overall data integrity during dimensionality reduction. Given these advantages, UMAP demonstrates superior performance over t-SNE and PCA in the context of reducing the dimensionality of Logging While Drilling (LWD) data, making it a more appropriate choice for subsequent visualization and analysis tasks (Figure 8). Based on a comprehensive evaluation of intra-class compactness, inter-class separability, and the Davies-Bouldin Index (DBI), UMAP exhibits a distinct advantage over PCA and t-SNE in the task of lithology identification (Table 3). UMAP achieves an average within-class compactness of 7.86, comprising values of 7.10 for silt and 11.36 for coarse sand, along with an average inter-class discrimination score of 6.91. These results demonstrate UMAP's ability to effectively capture both local details and global structures, particularly in geologically complex transition zones such as the interface between silt and fine sandstone. As a nonlinear dimensionality reduction technique, UMAP alleviates the lithological overlap caused by PCA's linear compression (e.g., DBI of 3.33 for UMAP versus 3.06 for PCA in the case of shale and limestone) and avoids the excessive dispersion and cluster fragmentation associated with t-SNE's tendency to overfit local structures. Furthermore, UMAP projections exhibit strong consistency with known sedimentary patterns, such as the  
Figure 8: Visualization of the dataset after dimensionality reduction: (**a**) PCA dimensionality reduction results, (**b**) t-SNE dimensionality reduction results, and (**c**) UMAP dimensionality reduction result. energy gradient across silt, fine-grained, and coarse sand, as well as lithological provenance, such as the isolated distribution of limestone. Overall, UMAP provides a high-fidelity, interpretable low-dimensional feature space that facilitates accurate real-time identification of highly heterogeneous formations during drilling operations. A comprehensive evaluation based on intra-class compactness, inter-class discrimination, and the Davies-Bouldin Index (DBI) reveals that UMAP provides substantial advantages in lithology identification tasks (Figure 9). Specifically, UMAP achieves an average intra-class compactness of 5.67-6.19 for coarse sand and 4.55 for clay shale, along with an average inter-class discrimination score of 5.03.