Graph structure consistency and pseudo-label guided source-free domain adaptation for lithology identification

Existing deep learning-based lithology identification methods encounter several critical bottlenecks: they fail to achieve robust model transfer in source-free scenarios, underutilize the intrinsic topological information of data, and are susceptible to noisy pseudo-labels. To address these challenges, a novel framework termed graph structure consistency and pseudo-label guided source-free domain adaptation (GCPG-SFDA) for lithology identification is proposed. This framework integrates graph-based modeling with the mean teacher framework to build a multi-dimensional system for feature optimization and knowledge transfer. Specifically, tabular lithological data is first transformed into graph structures and processed via graph neural network, enabling the extraction of high-order semantic relationships while preserving original features to enhance target-domain representation. Furthermore, three relationship graphs (teacher graph, student graph, and teacher-student graph) are designed. Graph consistency constraints optimize sample similarities in the feature space, improving the clarity of target domain classification boundaries. Meanwhile, a self-supervised exploration mechanism is implemented to encourage robust feature learning through structural perturbations and teacher-student output synchronization. Comprehensive evaluations indicate that GCPG-SFDA achieves superior performance in lithology identification, offering a robust solution for data-constrained geological tasks.