  Over the year, people have been using deep learning to tackle inversion
problems, and we see the framework has been applied to build relationship
between recording wavefield and velocity (Yang et al., 2016). Here we will
extend the work from 2 perspectives, one is deriving a more appropriate loss
function, as we now, pixel-2-pixel comparison might not be the best choice to
characterize image structure, and we will elaborate on how to construct cost
function to capture high level feature to enhance the model performance.
Another dimension is searching for the more appropriate neural architecture,
which is a subset of an even bigger picture, the automatic machine learning, or
AutoML. There are several famous networks, U-net, ResNet (He et al., 2016) and
DenseNet (Huang et al., 2017), and they achieve phenomenal results for certain
problems, yet it's hard to argue they are the best for inversion problems
without thoroughly searching within certain space. Here we will be showing our
architecture search results for inversion.
