Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder

• Published in: Physics of the earth and planetary interiors Vol. 309; p. 106584
• Main Authors: Cheng, Xianqiong, Jiang, Kezhi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2020
• Subjects: Conditional variational autoencoder; Crustal model; Data-driven; Eastern Qinghai-Tibet plateau and western Yangtze craton; Conditional variational autoencoder; Data-driven; Crustal model; Eastern Qinghai-Tibet plateau and western Yangtze craton
• ISSN: 0031-9201; 1872-7395
• DOI: 10.1016/j.pepi.2020.106584

Eastern margin of the Qinghai-Tibet plateau and western margin of the Yangtze craton has a complex crust-mantle structure and is an important region for studying the crust-mantle deformation mechanism. Because the Rayleigh surface wave group velocity has a strong constraint on the structure of the crust and upper mantle, a lot of researches have used the Rayleigh surface wave group velocity to invert the crust-mantle structure in this region, but the layered multi-parameter joint inversion and the uncertainty of the inversion result evaluation is still a research hotspot. Motived by Conditional Variational Autoencoder (CVAE) which is composed of a decoding and encoding process that uses a deep neural network to predict the variational distribution of parameters, we propose a crustal model by CVAE to attain the forward and inversion relationship between the group velocity of Rayleigh wave and the crustal model. In which the CVAE decoding corresponds to the forward process and the encoding corresponds to the inverse process. Moreover, the inversion results of this method are constrained from three aspects as follows: The network inputs and outputs have the same group velocities; The decoded outputs are normal distribution of hidden variables representing the crustal model; The mean values of the decoded output during CVAE training are the sampled crustal models. The proposed method can invert multi-parameters and evaluate uncertainty of the inversion efficiently. Based on the latest group velocity model, we invert for mean and variance of thickness in the upper, middle, and lower sedimentary cover, and of thickness, P-wave velocity, S-wave velocity and density in the upper, middle, and lower crystalline crust for eastern Qinghai-Tibet plateau and western Yangtze craton. Compared with the existing crustal models obtained by different methods, the results of this study are in good agreement. We conclude that CVAE-based group velocity inversion of the crustal model is a feasible and reliable method. •CVAE can deal with geophysical forward and inversion problem.•CVAE can find relationship between Rayleigh surface wave group velocity and crust structure.•Reliable crust model maps can be achieved in the study area.