  The accurate and fast estimation of velocity models is crucial in seismic
imaging. Conventional methods, like Tomography and Full-Waveform Inversion
(FWI), obtain appropriate velocity models; however, they require intense and
specialized human supervision and consume much time and computational
resources. In recent years, some works investigated deep learning(DL)
algorithms to obtain the velocity model directly from shots or migrated angle
panels, obtaining encouraging predictions of synthetic models. This paper
proposes a new flow to increase the complexity of velocity models recovered
with DL. Inspired by the conventional geophysical velocity model building
methods, instead of predicting the entire model in one step, we predict the
velocity model iteratively. We implement the iterative nature of the process
when, for each iteration, we train the DL algorithm to determine the velocity
model with a certain level of precision/resolution for the next iteration; we
name this process as Deep-Tomography. Starting from an initial model that
roughly approaches the true model, the Deep-Tomography is able to predict an
appropriate final model, even in complete unseen data, like the Marmousi model.
