## 4 Results
### Lithology Classification Feature Extraction
Cluster 4 is composed entirely of fine sandstone, representing a homogeneous lithology, while cluster 6 contains 44.8% clay shale and 27.1% limestone, indicating a transitional or mixed deposit between clay shale and limestone. Clusters with high proportions of coarse sand, such as cluster 7 (74.1% coarse sand), likely indicate high-energy environments, such as fluvial or littoral settings. Conversely, clusters with a high percentage of clay shale, like cluster 1 (41.4% clay shale), may reflect low-energy environments, such as lake or deep-sea deposits. Clusters containing a large proportion of limestone, such as cluster 14 (85.8% limestone), likely correspond to carbonate platform or reef environments. Notably, some clusters exhibit unusual distributions that require further consideration. For instance, cluster 13 is dominated by coarse sand (79.1%), but also contains small amounts of other lithologies, which may suggest local variations in a coarse sand-dominated environment. Similarly, cluster 5 consists of 80.9% fine sand, with 12.4% limestone, possibly representing a sandy deposit with limestone fragments or cementing. Additionally, certain clusters  
Figure 10: Davies–Bouldin Index for each class with a different number of clusters. may represent transitional phases within the sedimentary sequence, such as the mixture of fine sand (81.6%) and silt (15.5%) in cluster 9, which could reflect an environment with fluctuating hydrodynamic conditions. Although the original lithology classification consists of six categories, actual formations often exhibit multi-scale sub-category structures due to variations in sedimentary microfacies, diagenesis, or engineering responses. For instance, clay shale can be further divided into deep-water organic enrichment facies, coastal calcareous cementing facies, and tidal flat biodisturbance facies, each displaying distinct logging responses (e.g., GR, resistivity) and engineering parameters (e.g., drilling time, torque). Similarly, coarse sands can be differentiated into loose channel sand and tightly cemented alluvial fan sand based on gravel content and cementation degree, which are naturally separated in the UMAP-reduced space. Furthermore, thin reservoirs--such as sand--claystone transition zones--are classified as a single lithological unit in the original labeling. However, the actual data distribution reveals a more gradual transition. By increasing the number of clusters, the algorithm effectively captured this continuous change in sedimentary energy. Thus, the clustering result for K = 15 aligns with geological understanding, revealing lithological sub-classes and microfacies information that manual classification fails to capture. This pro  
Figure 11: Clustering results under the optimal number of clusters (K = 15). Figure 12: The number of lithological labels of each class contained in each cluster. vides a higher-resolution approach for describing reservoir heterogeneity and significantly enhances the geological and engineering applicability of the lithology classification model. Based on the distribution characteristics of acoustic time difference (DT) and torque (TQA), both of which are sensitive to lithology, the clustering results reveal the differentiation patterns of sedimentary environments for various lithology assemblages (Figure 13). Clusters with high DT values (e.g., cluster 1, median DT 91.92, IQR = 8.57) and low DT values (e.g., cluster 13, median DT 69.13, IQR = 5.19) correspond to high-porosity sand bodies and tightly cemented calcareous formations, respectively. The elevated DT value in cluster 1 reflects the gradual hydrodynamic changes characteristic of a pre-delta silty sand transition zone, while the low TQA fluctuations indicate homogeneous suspension deposition. Conversely, the very low DT value of cluster 13 aligns with the characteristics of coarse-grained sand, suggesting that the coastal sandy beach is formed through wave sorting of low-porosity, compact sand bodies.