  Full waveform inversion (FWI) strongly depends on an accurate starting model
to succeed. This is particularly true in the elastic regime: The cycle-skipping
phenomenon is more severe in elastic FWI compared to acoustic FWI, due to the
short S-wave wavelength. In this paper, we extend our work on extrapolated FWI
(EFWI) by proposing to synthesize the low frequencies of multi-component
elastic seismic records, and use those "artificial" low frequencies to seed the
frequency sweep of elastic FWI. Our solution involves deep learning: we
separately train the same convolutional neural network (CNN) on two training
datasets, one with vertical components and one with horizontal components of
particle velocities, to extrapolate the low frequencies of elastic data. The
architecture of this CNN is designed with a large receptive field, by either
large convolutional kernels or dilated convolution. Numerical examples on the
Marmousi2 model show that the 2-4Hz low frequency data extrapolated from
band-limited data above 4Hz provide good starting models for elastic FWI of
P-wave and S-wave velocities. Additionally, we study the generalization ability
of the proposed neural network over different physical models. For elastic test
data, collecting the training dataset by elastic simulation shows better
extrapolation accuracy than acoustic simulation, i.e., a smaller generalization
gap.
