Inversion of crustal thickness in Chinese mainland based on deep denoising autoencoder neural network

Chinese mainland is a composite continent composed of many micro-blocks, fold belts and orogenic belts after evolution over a long geological period. Chinese mainland crust is of complex crust-mantle structure, and crustal thickness is one of the most important parameters. At the same time, Rayleigh surface wave group velocity and phase velocity have strong constraints on the crust and upper mantle structure. In this paper, a data driven, named deep denoising autoencoder （DDAE） neural network is used to explore the relationship of forward and inversion between Rayleigh wave group velocity and phase velocity and crustal thickness, and we invert the crustal thickness of Chinese mainland by the latest dispersion model. In this paper, the evaluation of neural network architecture is put forward. In addition to the traditional test error and training error, it is compared with the result of network prediction with the forward process of the known physical principles. When designing the network architecture, the forward and inverse problems of the earth model and the surface wave dispersion are considered simultaneously, that is, the corresponding forward process is decoded, and the encoding process corresponds to the inversion process. At the same time, in view of the noise characteristics of the observed dispersion data, the training sample is polluted by noise, so the decoder decodes the noise-free input to denoising the observed data. After debugging, analyzing and optimizing the various parameters of the network, a robust neural network was finally obtained, and the crust thickness of the Chinese mainland was reproduced accordingly. The results of this study are in good agreement with the crustal thickness models obtained by different methods, suggesting that the deep denoising autoencoder neural network can well reveal the nonlinear relationship between surface wave dispersion and crustal thickness, and it is a feasible and credible method to solve the problem of surface wave dispersion inversion for crustal thickness.