Inverting Rayleigh surface wave velocities for eastern Tibet and western Yangtze craton crustal thickness based on deep learning neural networks

Crustal thickness is an important factor affecting lithosphere structure and therefore deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse 10 auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities 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 15 study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we conclude that deep learning neural network is a promising, believable and inexpensive tool for geophysical inversion.