Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network

Abstract

Recent advancements in Artificial Intelligence (AI), particularly deep learning, have significantly improved lithology identification in reservoir exploration by leveraging micrographic rock imagery. Deep neural networks excel in feature extraction, enhancing classification accuracy. However, these models are prone to domain shifts, which often degrade their performance in real-world applications. This paper proposes an unsupervised domain adaptation framework that integrates Fisher linear discriminant analysis and Online Hard Example Mining (OHEM) to mitigate domain shifts and improve classification, particularly in datasets with imbalanced classes. The model employs a $\omega$-balanced global–local domain discriminator to align feature distributions between different domains and introduces focal loss with class-wise weighted factors for better handling of imbalanced data. Additionally, an adapted version of OHEM identifies difficult samples during training, allowing the model to concentrate on challenging cases. The proposed method is validated on micrographic rock imagery from the Tibet, Qinghai, and Xinjiang regions, achieving an average accuracy of 83.2%, which is 13.8% higher than ResNet50 and at least 1% superior to other domain adaptation models. This research highlights the potential of AI-driven solutions in geoscientific applications and provides a robust framework for unsupervised lithology classification.