Rayleigh-wave dispersion data selection and model fine-tuning based on uncertainty estimation

Rayleigh-wave inversion is a reliable approach for obtaining subsurface shear-wave velocity structures and holds significant importance in seismic risk assessment, resource exploration, and geotechnical engineering. Numerous studies have demonstrated the great potential of deep learning (DL) in Rayleigh-wave inversion; however, existing DL methods still suffer from limited generalization, strong dependence on training data, and slow convergence. To address these issues, this study proposes a representative data selection and model optimization strategy. Specifically, we identify high-uncertainty samples based on the inconsistency of predictions from multiple pretrained models trained in parallel. An automatic differentiation-driven inversion method is then used to generate high-confidence pseudo-labels for the selected data, which are subsequently employed to fine-tune the original model. This workflow requires no borehole information and significantly improves the prediction accuracy and robustness of the model in the target area. Both synthetic and field experiments validate the effectiveness of the proposed method, demonstrating enhanced adaptability and performance in complex geological environments with relatively small additional cost.