## 1 Introduction  
In geological exploration and resource development, identifying target horizons based on the well logging data is a critical step [1]. Target horizons refer to specific geological layers, such as sandstone, coal seams, and limestone, which often contain valuable resources like oil, natural gas, coal, and minerals [2]. Accurately defining the extent of coal is important for safety, resource estimation, and mine design, and helps optimize mining and oil extraction plans and minimize resource waste. In addition, sandstone and limestone are common reservoir rocks that often contain abundant oil and natural gas resources. The lithology identification from well logging is to establish the relationship between the petrological characteristics and the logging curves. Typical lithologies are supposed to have their own specific logging responses [3]. For example, coal seams typically have low mass density and exhibit natural gamma density because they are rich in organic matter, resulting in low levels of radioactive elements. Sandstone, on the other hand, has high resistivity and, if it is free of impurities or water, generally shows low natural gamma content and higher density. Pure limestone typically has low natural gamma values because it contains few radioactive clays, and it exhibits high resistivity and mass density [4; 5; 6]. However, traditional logging interpretation relies extensively on expert knowledge and experience, making it labor-intensive and time-consuming. Additionally, it is often affected by subjectivity and inconsistency in expert judgment [7]. Sometimes, well logging interpretation is based on the mathematical and physical modeling of the processes under study, statistical methods (correlation and discrimination), solving systems of non-linearequations petrophysics (inverse problem of geophysics), and some other linear statistical methods. The complexities inherent in the geological conditions of a combination of different rock types, such as muddy sandstone, muddy limestone, carbonate, tight sandstone, or sandy conglomerate reservoirs [8; 9], the growing diversity, and volume of logging data highlight the significant limitations of traditional interpretation methods. In recent years, in the face of massive geophysical data processing and interpretation, deep learning (DL) methods have provided new approaches for utilizing and analyzing data [10; 11; 12; 13]. Due to the data-driven nature of deep learning methods, which do not rely on physical models, once the model is trained well, it can establish a rapid mapping from the observed data to the predicted parameters. As a result, automatic interpretation techniques of well logging curves using deep learning methods exhibit extremely high efficiency. In the past decades, different statistical and machine learning approaches have been used to automatedly predict lithotypes directly using a combination of geophysical well log data in petroleum wells [14; 15; 16; 17]. Generally, the identification of coal intervals or other different lithology is not a difficult issue, and several studies have already revealed that different machine learning approaches can accurately differentiate coal from other non-coal lithologies due to its distinguishing low density and gamma ray ranges [18; 19; 20; 21; 22]. Recently, more studies have attempted to compare the performance of machine learning methods for lithology identification [23; 24; 25]. The results showed that Random Forest has the best score among considered algorithms. However, most of these studies rely on a limited number of well log curves in the same geological area, with sample sizes ranging from hundreds to a few thousand, which are insufficient to establish a generalized lithofacies prediction model. Typically, well log data in these studies originate from the same region, raising concerns about the transferability of trained models to other areas.