  An accurate velocity model is essential to make a good seismic image.
Conventional methods to perform Velocity Model Building (VMB) tasks rely on
inverse methods, which, despite being widely used, are ill-posed problems that
require intense and specialized human supervision. Convolutional Neural
Networks (CNN) have been extensively investigated as an alternative to solve
the VMB task. Two main approaches were investigated in the literature:
supervised training and Physics-Informed Neural Networks (PINN). Supervised
training presents some generalization issues since structures, and velocity
ranges must be similar in training and test set. Some works integrated
Full-waveform Inversion (FWI) with CNN, defining the problem of VMB in the PINN
framework. In this case, the CNN stabilizes the inversion, acting like a
regularizer and avoiding local minima-related problems and, in some cases,
sparing an initial velocity model. Our approach combines supervised and
physics-informed neural networks by using transfer learning to start the
inversion. The pre-trained CNN is obtained using a supervised approach based on
training with a reduced and simple data set to capture the main velocity trend
at the initial FWI iterations. We show that transfer learning reduces the
uncertainties of the process, accelerates model convergence, and improves the
final scores of the iterative process.
