We propose a method of extrapolated full waveform inversion (EFWI), where FWI is allowed to make use of data augmented by increasing its frequency band with a convolutional neural network (CNN). The proposed deep-learning method of low-frequency extrapolation shows adequate generalizability for the initialization step of EFWI. Numerical examples show that the neural network trained on several submodels of the Marmousi model is able to predict the low frequencies for the BP 2004 benchmark model.