  Geological interpretation of seismic images is a visual task that can be
automated by training neural networks. While neural networks have shown to be
effective at various interpretation tasks, a fundamental challenge is the lack
of labeled data points in the subsurface. For example, the interpolation and
extrapolation of well-based lithology using seismic images relies on a small
number of known labels. Besides well-known data augmentation techniques, as
well as regularization of the network output, we propose and test another
approach to deal with the lack of labels. Non learning-based horizon trackers
work very well in the shallow subsurface where seismic images are of higher
quality and the geological units are roughly layered. We test if these
segmented and shallow units can help train neural networks to predict deeper
geological units that are not layered and flat. We show that knowledge of
shallow geological units helps to predict deeper units when there are only a
few labels for training using a dataset from the Sea of Ireland. We employ
U-net based multi-resolution networks, and we show that these networks can be
described using matrix-vector product notation in a similar fashion as standard
geophysical inverse problems.
