A Symmetry-Aware GGA-XGB Model for Lithology Prediction Under Complex Geological Conditions

Lithology prediction is a fundamental component of geological exploration and hydrocarbon reservoir characterization, playing a critical role in improving subsurface structural interpretation and enhancing resource prediction accuracy. However, well log data are typically characterized by high dimensionality, strong nonlinearity, severe class imbalance, and asymmetric geological feature distributions, which significantly restrict the predictive accuracy and generalization capability of conventional machine learning methods. To address these challenges, this study proposes a symmetry-aware lithology classification framework based on a Hybrid Grey Wolf Optimizer–Genetic Algorithm optimized Extreme Gradient Boosting (GGA-XGB) model. The proposed framework establishes a symmetric collaborative optimization mechanism by integrating the global exploration capability of the Grey Wolf Optimizer (GWO) with the local exploitation ability of the Genetic Algorithm (GA), thereby achieving a balanced optimization strategy between exploration and exploitation. Specifically, GWO first performs coarse-grained global hyperparameter optimization of XGBoost to improve search efficiency and optimization stability, while GA subsequently refines the parameter space to further enhance local optimization accuracy. Experimental results on a multi-class well logging dataset demonstrate that the proposed method achieves outstanding classification performance, with precision, recall, and F1-score all reaching 0.9862. Compared with several conventional machine learning methods, the proposed GGA-XGB framework exhibits superior predictive accuracy. The symmetry-aware optimization strategy provides an effective solution for intelligent lithology prediction under complex geological conditions and offers both theoretical insights into symmetry-aware optimization mechanisms and practical value for intelligent geoscience and hydrocarbon exploration.