13 citations as recorded by crossref.

1. Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data J. Hu et al. 10.1785/0220190222
2. Constructing shear velocity models from surface wave dispersion curves using deep learning Y. Luo et al. 10.1016/j.jappgeo.2021.104524
3. 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
4. All-parameters Rayleigh wave inversion X. Yang & K. Yuen 10.1007/s11803-021-2036-5
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
6. An artificial neural network approach for the inversion of surface wave dispersion curves A. Yablokov et al. 10.1111/1365-2478.13107
7. 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
8. A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data M. Aleardi & E. Stucchi 10.1002/nsg.12163
9. 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
10. Deep learning inversion of Rayleigh-wave dispersion curves with geological constraints for near-surface investigations X. Chen et al. 10.1093/gji/ggac171
11. A homotopy inversion method for Rayleigh wave dispersion data P. Ping et al. 10.1016/j.jappgeo.2022.104914
12. Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions F. Wang et al. 10.1029/2022JB023944
13. Deep Learning for Geophysics: Current and Future Trends S. Yu & J. Ma 10.1029/2021RG000742