Lithology identification based on ramified structure model using generative adversarial network for imbalanced data

Lithology identification plays a crucial role in the process of geological exploration and oil and gas development. Currently, the primary challenges in lithology identification include the inadequate differences of adjacent lithologies and the imbalance of lithology samples. This paper focuses on addressing these issues from three aspects. Firstly, a ramified lithology identification model based on neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) is established to solve the problem of poor identification effect of adjacent lithology of single model. The task of lithology identification is split through a group network, and several identification models are designed to learn the subtle differences of adjacent lithologies. Secondly, to deal with the issue of ineffective feature learning due to limited samples, a data augmentation strategy is proposed. This strategy combines K -means SMOTE algorithm and Wasserstein conditional generative adversarial network (WCGAN). Finally, the k -nearest neighbor algorithm (KNN) is employed as a data screening method. Compared with other machine learning methods, the ramified lithology identification model demonstrates superior performance, particularly in identifying adjacent lithologies, on the well logging dataset from Kansas. Furthermore, the data augmentation strategy effectively enhances the identification accuracy of classes with fewer samples, thereby enhancing the overall accuracy of lithology identification.