Research and application of wavelet neural network in electrical resistivity imaging inversion

In order to obtain high-quality electrical resistivity imaging (ERI) inversion results, this paper proposes a wavelet-based neural network inversion method. In addition, it is applied to 2D electrical resistivity tomography imaging. Firstly, a sample set suitable for wavelet neural network training is designed to optimize neural network parameters and improve the accuracy of electrical resistivity inversion. Secondly, a hybrid multi-layer wavelet neural network is designed, which uses Mexican-hat and Morlet wavelets as activation functions for different hidden layers, to improve the generalization ability and stability of the network. Finally, the superiority of the WNN is verified through two experiments and applied for inversion in field examples. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.