  In salt provinces, full-waveform inversion (FWI) is most likely to fail when
starting with a poor initial model that lacks the salt information.
Conventionally, salt bodies are included in the FWI starting model by
interpreting the salt boundaries from seismic images, which is time-consuming
and prone to error. Studies show that FWI can improve the interpreted salt
provided that the data are recorded using long offsets, and contain low
frequencies, which are not always available. Thus, we develop an approach to
invert for the salt body starting from a poor initial model, limited data
offsets, and the absence of low frequencies. We leverage deep learning to apply
multi-stage flooding and unflooding of the velocity model. Specifically, we
apply a multi-scale FWI using three frequency bandwidths. We apply a network
after each frequency scale. After the first two bandwidths, the networks are
trained to flood the salt, while the network after the last frequency bandwidth
is trained to unflood it. We verify the method on the synthetic BP 2004 salt
model benchmark. We only use the synthetic data of short offsets up to 6 km and
remove frequencies below 3 Hz. We also apply the method to real vintage data
acquired in the Gulf of Mexico region. The real data lack frequencies below 6
Hz and the streamer length is only 4.8 km. With these limitations, we manage to
recover the salt body and verify the result by using them to image the data and
analyze the resulting angle gathers.
