A Sample Selection Method for Neural-network-based Rayleigh Wave Inversion

Rayleigh wave inversion is a reliable method for inverting the S-wave velocities to reflect the stiffness status of the soil and rock masses of the subsurface. The optimization potential of neural networks in the inversion task is gaining recognition among researchers. Regarding neural-network-based Rayleigh wave inversion, a closer functional relationship between the training samples and the unknown function to be modeled indicates improved inversion performance. The traditional sampling method involves randomly generating samples within a predefined search space, which can result in some samples deviating from the actual functional relationship, thus reducing the accuracy and stability of the inversion. However, few studies consider the sample selection issue in the inversion process based on neural networks. This study proposes a sample selection method for selecting more appropriate training samples to overcome the neglect of sample selection, enhancing the functional modeling of neural networks for Rayleigh wave inversion. The implementation of the proposed sample selection method involves two procedures. First, the random samples are generated within a predefined search space to create a pool of samples. Afterward, the mean moving correlation coefficients of the samples inside the pool are calculated to select more suitable samples for network training based on the moving correlation calculation. Numerical simulations and field data applications demonstrate the necessity and effectiveness of the proposed sample selection method for neural-network-based Rayleigh wave inversion. It is concluded that the proposed method effectively enhances the performance of S-wave velocity estimation through Rayleigh wave inversion using neural networks.