Abstract. Crustal thickness is an important factor affecting lithosphere structure and deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities of Rayleigh surface wave simultaneously as input and theoretical crustal thickness as output, we construct twelve deep neural networks trained by 70,000 and tested by 30,000 theoretical models. We then invert observed phase and group velocities by these twelve neural networks. Based on test errors and misfits with other crustal thickness models, we select the optimized one as crustal thickness for study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we adopt a new way for inversion of earth model parameters, and realize that deep learning neural network based on data driven with the highly nonlinear mapping ability can be widely used by geophysical inversion method, and our result has good agreement with high-resolution crustal thickness models. We conclude that deep learning neural network is a promising, efficient and believable tool for geophysical inversion.
Bayesian geophysical inversion using invertible neural networks
