  Seismic full-waveform inversion (FWI), which uses iterative methods to
estimate high-resolution subsurface models from seismograms, is a powerful
imaging technique in exploration geophysics. In recent years, the computational
cost of FWI has grown exponentially due to the increasing size and resolution
of seismic data. Moreover, it is a non-convex problem and can encounter local
minima due to the limited accuracy of the initial velocity models or the
absence of low frequencies in the measurements. To overcome these computational
issues, we develop a multiscale data-driven FWI method based on fully
convolutional networks (FCN). In preparing the training data, we first develop
a real-time style transform method to create a large set of synthetic
subsurface velocity models from natural images. We then develop two
convolutional neural networks with encoder-decoder structure to reconstruct the
low- and high-frequency components of the subsurface velocity models,
separately. To validate the performance of our data-driven inversion method and
the effectiveness of the synthesized training set, we compare it with
conventional physics-based waveform inversion approaches using both synthetic
and field data. These numerical results demonstrate that, once our model is
fully trained, it can significantly reduce the computation time, and yield more
accurate subsurface velocity models in comparison with conventional FWI.
