Constructing shear velocity models from surface wave dispersion curves using deep learning

Luo，Yinhe1,2; Huang，Yao1; Yang，Yingjie3; Zhao，Kaifeng1; Yang，Xiaozhou1; Xu，Hongrui1

2022

10.1016/j.jappgeo.2021.104524

JOURNAL OF APPLIED GEOPHYSICS   影响因子和分区

0926-9851

Surface wave tomography has been widely used to determine shear wave velocities by inverting surface wave dispersion curves. Conventional least-squares inversions strongly depend on an initial model and Monte Carlo inversion algorithms are usually time-consuming. In this study, we apply a deep neural network (DNN) to surface wave dispersion curves to investigate whether the initial model can be relaxed and whether reliable shear velocity models can be constructed. By applying our method to synthetic and field data, our results show that: (1) by constructing a well-trained DNN model from the global continental CRUST1.0 data, the DNN approach is effective and efficient to determine shear velocity structures using Rayleigh wave dispersion curves; (2) using the well-trained DNN model, no prior model is required, relaxing the requirement of an initial model; (3) the well-trained DNN model can be used to construct pseudo 3D seismic models across different continental areas.

And shear velocity structure Deep neural network Dispersion curve Surface wave tomography

EI

英语

其他

National Key Research and Development Program of China[2017YFC1500302];National Natural Science Foundation of China[41874058];National Natural Science Foundation of China[42074055];

20215211401460

Dispersion (waves) ; Shear flow ; Shear waves ; Surface waves ; Tomography ; Velocity

Ergonomics and Human Factors Engineering:461.4 ; Fluid Flow, General:631.1 ; Imaging Techniques:746 ; Mechanics:931.1

GEOSCIENCES

2-s2.0-85121732221

Scopus

1.Hubei Subsurface Multi-scale Imaging Key Laboratory,Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan,430074,China
2.State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan,430074,China
3.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,China

Luo，Yinhe,Huang，Yao,Yang，Yingjie,et al. Constructing shear velocity models from surface wave dispersion curves using deep learning[J]. JOURNAL OF APPLIED GEOPHYSICS,2022,196.

Luo，Yinhe,Huang，Yao,Yang，Yingjie,Zhao，Kaifeng,Yang，Xiaozhou,&Xu，Hongrui.(2022).Constructing shear velocity models from surface wave dispersion curves using deep learning.JOURNAL OF APPLIED GEOPHYSICS,196.

Luo，Yinhe,et al."Constructing shear velocity models from surface wave dispersion curves using deep learning".JOURNAL OF APPLIED GEOPHYSICS 196(2022).