## 1 Introduction  
With the ongoing advancement in the exploration and development of oil and gas resources, geological targets have transitioned from traditional structural and integral reservoirs to complex reservoirs characterized by intricate structures, significant stratigraphic heterogeneity, and thin effective reservoir layers. This geological complexity heightens the challenges in reservoir prediction and introduces greater technical challenges for drilling operations. Consequently, this evolution underscores the growing need for advanced intelligent, real-time formation prediction and geosteering technologies to ensure precise well placement within structurally and lithologically complex environments [1, 2, 3]. Logging While Drilling (LWD) has emerged as a pivotal technology in contemporary geophysical workflows, offering real-time petrophysical measurements during drilling operations. These data enable timely updates to geological models and enhance decision making for directional drilling by providing critical insights [4, 5, 6, 7, 8]. However, a significant challenge in this context remains the real-time identification of reservoir lithology, a crucial component for achieving effective model correction and precise drilling trajectory adjustment [9, 10, 11]. While traditional empirical methods frequently fail to adequately address the complexity and heterogeneity of subsurface formations, recent advancements have integrated machine learning techniques that leverage multi-source LWD and drilling engineering data [12, 13, 14, 15, 16]. These data-driven models have demonstrated exceptional capabilities for real-time lithology classification, thus significantly improving formation interpretation and geosteering accuracy in complex geological settings [17, 18, 19]. Compared with conventional wireline logging techniques, Logging While Drilling (LWD) provides substantial advantages for subsurface characterization [20, 21]. LWD tools enable real-time measurements during the drilling process, such as azimuthal gamma ray, azimuthal resistivity, photoelectric factor, nuclear magnetic resonance, acoustic velocity, neutron porosity, and formation density [22, 23, 24, 25, 26]. However, the quality and stability of these measurements are often compromised by downhole vibration phenomena, including stick-slip, bit-bounce, and whirl, which can shorten tool lifespan and degrade data quality. These vibration loads not only interfere with accurate signal acquisition but also increase the uncertainty in lithology interpretation, highlighting the importance of robust data cleaning, dimensionality reduction, and modeling strategies for reliable formation identification [27, 28, 29]. Machine learning techniques have demonstrated significant advantages over traditional methods for lithology identification using LWD data. In contrast with empirical or rule-based approaches, machine learning algorithms are capable of processing vast amounts of heterogeneous data, capturing intricate nonlinear relationships, and revealing parameter interactions that are typically challenging to model explicitly. Such capabilities result in more accurate and robust lithology recognition outcomes [30, 31, 32]. [PERSON] et al. (2020) applied XGBoost, artificial neural networks (ANNs), and Random Forest (RF) models to classify lithologies in coal-bearing formations using both drilling engineering and LWD data, combined with data balancing techniques such as NearMiss under-sampling (NROS) and SMOTE [33]. Measurements-while-drilling (MWD) variables such as rate of penetration (ROP), weight on bit (WOB), and other real-time surface parameters have been utilized to predict electrical facies using supervised classification algorithms that link surface measurements to subsurface lithologies. [PERSON] et al. developed a bit-level lithology prediction model based on LWD parameters using several machine learning algorithms, including one-vs-one Support Vector Machines (OVO-SVMs), RF, neural networks, and XGBoost [34]. [PERSON] et al.