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We have formulated elastic seismic full waveform inversion (FWI) within adeep learning environment. In our formulation, a recurrent neural network isset up with rules enforcing elastic wave propagation, with the wavefieldprojected onto a measurement surface acting as the synthetic data to becompared with observed seismic data. Gradients for iterative updating of anelastic model, with a variety of parameterizations and misfit functionals, canbe efficiently constructed within the network through the automaticdifferential method. With this method, the inversion based on complex misfitscan be calculated. We survey the impact of different complex misfits (based onthe l2, l1 ) with high order total variation (TV) regulations on multiparameterelastic FWI recovery of models within velocity/density, modulus/density, andstiffness parameter/density parameterizations. We analyze parameter cross-talk.Inversion results on simple and complex models show that the RNN elastic FWIwith high order TV regulation using l1 norm can help mitigate cross-talk issueswith gradient-based optimization methods.

Numerical analysis of a deep learning formulation of elastic full  waveform inversion with high order total variation regularization in  different parameterization