DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity Dispersion Curves

doi:doi:10.57702/lmmp7nwx

DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity Dispersion Curves

Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse problem is ill-posed, non-unique and nonlinear. Here, we introduce DispersioNET, a deep learning model based on convolution neural networks (CNN) to perform the joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves.

BibTex:

@dataset{Rohan_Sharma_and_Divakar_Vashisth_and_Bharath_Shekar_2024,
    abstract = {Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse problem is ill-posed, non-unique and nonlinear. Here, we introduce DispersioNET, a deep learning model based on convolution neural networks (CNN) to perform the joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves.},
    author = {Rohan Sharma and Divakar Vashisth and Bharath Shekar},
    doi = {10.57702/lmmp7nwx},
    institution = {No Organization},
    keyword = {'Convolutional neural networks', 'Deep learning', 'Rayleigh wave', 'Seismic inversion'},
    month = {dec},
    publisher = {TIB},
    title = {DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity Dispersion Curves},
    url = {https://service.tib.eu/ldmservice/dataset/dispersionet--joint-inversion-of-rayleigh-wave-multimode-phase-velocity-dispersion-curves},
    year = {2024}
}