## 4 Results
### Application of Lithology Identification Model  
Based on the workflow for real-time intelligent identification and precision drilling in strongly heterogeneous formations using multi-parameter LWD and drilling engineering data, the original dataset was split into a training set and a test set with a ratio of 80% to 20%. The training set was then processed using various dimensionality reduction methods to obtain the corresponding datasets. These included the original training data, PCA, t-SNE, UMAP, t-SNE + K-means, and UMAP + K-means datasets, which were further divided into training and validation sets with a ratio of 4:1. To enhance the model's generalization ability, a five-fold cross-validation method was applied, and a grid search was used to identify the optimal parameter combination for each model. To minimize the influence of random factors, the training process was repeated 10 times, and the average results were recorded. A line chart was generated to visualize the training performance of each model across the different datasets, allowing for a comparative analysis of how each dimensionality reduction method affected the model's performance.  
(1) Model training  
Four different classification models were developed using Support Vector Machine (SVM), Random Forest (RF), Lightweight Gradient Boosting Machine (LightGBM), and Symmetric Decision Tree Gradient Boosting Machine (CatBoost). The tuning parameters and their optimal values for each model are detailed in Table 4. For the SVM model, the tuning parameters include the regularization penalty coefficient C and the coefficient  
Figure 14: The lithology corresponding to each class of data points is calibrated according to the LWD logging parameters and imaging.  
\(\gamma\) of the RBF kernel function. Since Random Forest, LightGBM, and CatBoost all use decision trees as base classifiers, the tuning process focuses on parameters related to the tree structure. The most important of these is the minimum number of samples required to create leaf nodes. Additionally, Random Forest involves selecting both samples and features, making the maximum number of features another critical parameter. The CatBoost model, which is based on rank boosting, requires careful control of the learning rate during model iteration, as the model is trained on one subset of the data while calculating residuals on another subset.  
During the parameter tuning process, datasets that underwent only dimensionality reduction did not significantly improve the model. However, applying both dimensionality reduction and clustering processing enhanced the training performance. Among the various dimensionality reduction methods, the dataset processed with UMAP generally produced better model performance. Furthermore, the best training results were achieved using the dataset processed with UMAP + K-means.  
The optimal structural parameters with the highest score and strongest generalization ability for each model were determined through grid search and cross-validation. To assess the classification ability of the model for well logging lithology and evaluate the impact of the data feature enhancement method, the model with the optimal parameters was applied to the test dataset. The Balanced Accuracy and F1-score were then calculated to evaluate the model's prediction performance.