  Neural-networks have seen a surge of interest for the interpretation of
seismic images during the last few years. Network-based learning methods can
provide fast and accurate automatic interpretation, provided there are
sufficiently many training labels. We provide an introduction to the field
aimed at geophysicists that are familiar with the framework of forward modeling
and inversion. We explain the similarities and differences between deep
networks to other geophysical inverse problems and show their utility in
solving problems such as lithology interpolation between wells, horizon
tracking and segmentation of seismic images. The benefits of our approach are
demonstrated on field data from the Sea of Ireland and the North Sea.
