Unsupervised domain adaptation for lithology classification using dynamic entropy-based prototype learning

Lithology classification plays a crucial role in geological exploration and resource evaluation. However, the significant distribution discrepancies between source and target domains, coupled with the unavailability of source domain data, pose substantial challenges to traditional domain adaptation methods. To address these challenges, we propose an innovative framework, Unsupervised Domain Adaptive Dynamic Entropy Prototype Learning (UDADEPL), which leverages a source-free unsupervised domain adaptation strategy for lithology classification. The UDADEPL framework consists of a frozen source pre-trained model and a trainable target model, incorporating a dynamic entropy-based prototype learning matrix for reliable sample selection and centroid-based pseudo-label learning for iterative optimization. Additionally, an information maximization loss and source domain regularization loss are integrated into a curriculum learning strategy to balance feature extraction and domain adaptation. This approach enables the model to effectively handle complex lithological boundaries and class imbalances in the target domain. Extensive experiments on datasets from the Tarim Oilfield and Hugoton–Panoma field demonstrate the superiority of UDADEPL over traditional machine learning and advanced deep learning models. UDADEPL achieves superior classification accuracy, outperforming the best baseline models, especially in cross-domain adaptation and complex lithology identification.

Authors

• Hengxiao Li Tongji University
• Yahui Liu Kuaishou Technology
• Lu Liu

Keywords

• Lithology classification
• Geological exploration
• Unsupervised domain adaptive
• Dynamic entropy