  We have formulated elastic seismic full waveform inversion (FWI) within a
deep learning environment. In our formulation, a recurrent neural network is
set up with rules enforcing elastic wave propagation, with the wavefield
projected onto a measurement surface acting as the synthetic data to be
compared with observed seismic data. Gradients for iterative updating of an
elastic model, with a variety of parameterizations and misfit functionals, can
be efficiently constructed within the network through the automatic
differential method. With this method, the inversion based on complex misfits
can be calculated. We survey the impact of different complex misfits (based on
the l2, l1 ) with high order total variation (TV) regulations on multiparameter
elastic FWI recovery of models within velocity/density, modulus/density, and
stiffness parameter/density parameterizations. We analyze parameter cross-talk.
Inversion results on simple and complex models show that the RNN elastic FWI
with high order TV regulation using l1 norm can help mitigate cross-talk issues
with gradient-based optimization methods.
