Articles | Volume 26, issue 2
https://doi.org/10.5194/npg-26-61-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-26-61-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2019
Research article |
| 17 Apr 2019
| 17 Apr 2019
Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks
Xianqiong Cheng
CORRESPONDING AUTHOR
College of Geophysics, Chengdu University of Technology, Chengdu,
P.R. China
Qihe Liu
School of Information and Software Engineering, University of
Electronic Science and Technology of China, Chengdu, P.R. China
Pingping Li
College of Geophysics, Chengdu University of Technology, Chengdu,
P.R. China
Yuan Liu
College of Geophysics, Chengdu University of Technology, Chengdu,
P.R. China
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Cited
17 citations as recorded by crossref.
- Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data J. Hu et al. 10.1785/0220190222
- Constructing shear velocity models from surface wave dispersion curves using deep learning Y. Luo et al. 10.1016/j.jappgeo.2021.104524
- Reconstruction of the S-Wave Velocity via Mixture Density Networks With a New Rayleigh Wave Dispersion Function J. Yang et al. 10.1109/TGRS.2022.3169236
- All-parameters Rayleigh wave inversion X. Yang & K. Yuen 10.1007/s11803-021-2036-5
- A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland F. Wang et al. 10.1016/j.eqs.2023.02.007
- Deep Learning-Based Dispersion Spectrum Inversion for Surface Wave Exploration Y. Gan et al. 10.1109/TGRS.2024.3399033
- An artificial neural network approach for the inversion of surface wave dispersion curves A. Yablokov et al. 10.1111/1365-2478.13107
- Semi‐Supervised Surface Wave Tomography With Wasserstein Cycle‐Consistent GAN: Method and Application to Southern California Plate Boundary Region A. Cai et al. 10.1029/2021JB023598
- A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data M. Aleardi & E. Stucchi 10.1002/nsg.12163
- Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder X. Cheng & K. Jiang 10.1016/j.pepi.2020.106584
- High-Precision Sub-Seafloor Velocity Building Based on Joint Tomography and Deep Learning on OBS Data in the South China Sea G. Chen et al. 10.1007/s12583-025-0170-0
- Deep learning inversion of Rayleigh-wave dispersion curves with geological constraints for near-surface investigations X. Chen et al. 10.1093/gji/ggac171
- Detection of the low-velocity layer using a convolutional neural network on passive surface-wave data: An application in Hangzhou, China X. Chen et al. 10.1016/j.cageo.2024.105663
- A homotopy inversion method for Rayleigh wave dispersion data P. Ping et al. 10.1016/j.jappgeo.2022.104914
- Joint Inversion of Surface-Wave Dispersions and Receiver Functions Based on Deep Learning F. Wang et al. 10.1785/0220240040
- Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions F. Wang et al. 10.1029/2022JB023944
- Deep Learning for Geophysics: Current and Future Trends S. Yu & J. Ma 10.1029/2021RG000742
17 citations as recorded by crossref.
- Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data J. Hu et al. 10.1785/0220190222
- Constructing shear velocity models from surface wave dispersion curves using deep learning Y. Luo et al. 10.1016/j.jappgeo.2021.104524
- Reconstruction of the S-Wave Velocity via Mixture Density Networks With a New Rayleigh Wave Dispersion Function J. Yang et al. 10.1109/TGRS.2022.3169236
- All-parameters Rayleigh wave inversion X. Yang & K. Yuen 10.1007/s11803-021-2036-5
- A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland F. Wang et al. 10.1016/j.eqs.2023.02.007
- Deep Learning-Based Dispersion Spectrum Inversion for Surface Wave Exploration Y. Gan et al. 10.1109/TGRS.2024.3399033
- An artificial neural network approach for the inversion of surface wave dispersion curves A. Yablokov et al. 10.1111/1365-2478.13107
- Semi‐Supervised Surface Wave Tomography With Wasserstein Cycle‐Consistent GAN: Method and Application to Southern California Plate Boundary Region A. Cai et al. 10.1029/2021JB023598
- A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data M. Aleardi & E. Stucchi 10.1002/nsg.12163
- Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder X. Cheng & K. Jiang 10.1016/j.pepi.2020.106584
- High-Precision Sub-Seafloor Velocity Building Based on Joint Tomography and Deep Learning on OBS Data in the South China Sea G. Chen et al. 10.1007/s12583-025-0170-0
- Deep learning inversion of Rayleigh-wave dispersion curves with geological constraints for near-surface investigations X. Chen et al. 10.1093/gji/ggac171
- Detection of the low-velocity layer using a convolutional neural network on passive surface-wave data: An application in Hangzhou, China X. Chen et al. 10.1016/j.cageo.2024.105663
- A homotopy inversion method for Rayleigh wave dispersion data P. Ping et al. 10.1016/j.jappgeo.2022.104914
- Joint Inversion of Surface-Wave Dispersions and Receiver Functions Based on Deep Learning F. Wang et al. 10.1785/0220240040
- Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions F. Wang et al. 10.1029/2022JB023944
- Deep Learning for Geophysics: Current and Future Trends S. Yu & J. Ma 10.1029/2021RG000742
Latest update: 21 Feb 2025
Short summary
This paper is based on a deep learning neural network to invert the Rayleigh surface wave velocity of the crustal thickness, which is a new geophysical inversion solution that proved to be effective and practical. Through comparative experiments, we found that deep learning neural networks can more accurately reveal the non-linear relationship between phase velocity and crustal thickness than traditional shallow networks. Deep learning neural networks are more efficient than Monte Carlo methods.
This paper is based on a deep learning neural network to invert the Rayleigh surface wave...
