## 1 Introduction  
Deep and precise navigation technology directs drilling instruments to a specific location of the oil reservoir to acquire the optimum recovery ratio, and this is of great significance in improving the economic benefits of oil fields. The effect of lithology discrimination, especially the discrimination result of fine-scale reservoir sections, directly affects the development process of precise downhole navigation [1; 2; 3]. Traditional lithology discrimination methods rely on empirical rules and domain expertise, which are not only time-consuming but also often suffer from subjectivity and inconsistency issues [4; 5; 6; 7; 8]. Therefore, researchers are increasingly focusing on developing faster and more reliable identification tools [9; 10; 11; 12; 13]. Currently, artificial intelligence technology is gradually being adopted as an important alternative method to address such complex problems [14; 15; 16; 17; 18; 19]. Utilizing intelligent methods such as machine learning models to construct lithology discrimination models is highly effective. However, addressing issues in the data preprocessing stage is also important [20; 21; 22; 23]. Nowadays, various machine learning methods and related technologies are being applied in lithology discrimination tasks. These applications mainly include basic models such as naive Bayes classifier, support vector machine (SVM), and decision trees, as well as ensemble methods like random forest (RF) and gradient boosting decision trees (GBDT) [24; 25; 26; 27]. Through practical data testing and quantitative comparisons, ensemble methods exhibit outstanding performance in classifying sandstone-related lithologies that are difficult to distinguish accurately using other algorithms. Compared to weak classifiers, ensemble methods have broader application prospects [28; 29]. Building upon the foundation of algorithmic models, optimizing the model structure according to the specific requirements of practical problems aids in addressing more specialized and complex situations [30; 31]. Improving the random forest model with a probability-based fuzzy representation method is able to provide more information about rhythm, heterogeneity, and geological characteristics and improves the fineness of formation evaluation and reservoir characterization [32]. Introducing an enhanced multi-kernel Fisher discriminant model based on a single Fisher discriminant model effectively extracts features of carbonate reservoirs, enabling more precise lithology identification [33]. Artificial intelligence methods hold a comprehensive advantage in lithology discrimination tasks. The combination of various machine learning models with optimization algorithms forms an efficient and stable workflow for lithology discrimination. For instance, the fusion of principal component analysis, fuzzy decision tree models, and particle swarm optimization algorithms into lithology discrimination processing systems exemplifies this approach [34]. Additionally, there are methods that consist of dimensionality reduction, k-means sample clustering, and regression analysis for well logging lithology classification workflows [35; 36; 37]. These studies demonstrate the significant benefits of machine learning methods in lithology discrimination and formation evaluation. However, most of the research has not taken into account the impact of imbalanced well logging data distribution. Enhanced sampling algorithms can balance the dataset by adequately learning and accurately identifying classes with fewer samples in the original dataset. These methods improve data quality by considering both data distribution characteristics and model computation requirements [38]. The Gaussian mixture model-based over-sampling method with Jensen-Shannon divergence (GJ-RSMOTE) can effectively improve the prediction accuracy of C4.5 decision trees and SVM models [39].