CE-SGAN: Classification enhancement semi-supervised generative adversarial network for lithology identification

Lithology identification, the process of recognizing and distinguishing lithology using specific methods, is a fundamental task in the fields of formation evaluation and reservoir description. However, the lithology identification accuracy of traditional models is insufficient because the distribution of small-sample logging data is usually extremely unbalanced. In this paper, a classification enhancement semi-supervised generative adversarial network (CE-SGAN) model is proposed to mitigate the influence of data imbalance and improve the lithology identification accuracy. Considering the powerful nonlinearity of logging curves, a classification separation architecture is applied to attain an optimal equilibrium between the classifier and the generator. Furthermore, a pseudo-label processing mechanism is designed to enhance classification, which combined with semi-supervised learning. Experiment results on two small sample logging datasets demonstrate that the model provides a considerable improvement in lithology identification. Moreover, it is competitive in data enhancement.