Optimization and Evaluation of Ensemble Learning Models for Intelligent Lithology Identification Using Seismic Data

Lithology identification is one of the key geological interpretation tasks in oil and gas exploration, which directly affects the accuracy of reservoir modeling and resource evaluation. At present, seismic lithology prediction research has the limitation of over-focusing on the optimization of a single algorithm and lacking systematic comparison of ensemble models and hyperparameter strategies. To this end, this study employs recursive feature elimination, NM-SMOTE sampling, and four hyperparameter optimization methods. These are applied to well seismic data from the F3 exploration area in the North Sea to evaluate random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and stacked ensemble models (SEM).The experimental results show that in terms of hyperparameter optimization, the Optuna algorithm achieves the best balance between computational efficiency and model performance, and its optimization effect is significantly better than that of the traditional grid search method. In the context of single models, CatBoost shows the best prediction performance (AUC = 0.91), with clear boundaries for sandstone and mudstone identification and the best spatial continuity of the prediction results. The comparative analysis of different ensemble models shows that random forest has the highest stability, followed by LightGBM, while XGBoost is more sensitive to data noise, resulting in a instability in the prediction results. It is worth noting that the classification performance of the SEM is limited under complex geological conditions such as thin interbeds. This study systematically evaluates the technical characteristics of each model and proposes model selection criteria for different geological application scenarios, providing important theoretical basis and method support for the practical application of intelligent lithology identification technology.