DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity
  Dispersion Curves using Convolutional Neural Networks
Rohan Sharma Divakar Vashisth Bharath Shekar
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.
DispersioNET is trained and tested on both noise-free and noisy dispersion
curve datasets and predicts S-wave velocity profiles that match closely with
the true velocities. The architecture is agnostic to variations in S-wave
velocity profiles such as increasing velocity with depth and intermediate
low-velocity layers, while also ensuring that the output remains independent of
the number of layers.