  Geologic interpretation of large seismic stacked or migrated seismic images
can be a time-consuming task for seismic interpreters. Neural network based
semantic segmentation provides fast and automatic interpretations, provided a
sufficient number of example interpretations are available. Networks that map
from image-to-image emerged recently as powerful tools for automatic
segmentation, but standard implementations require fully interpreted examples.
Generating training labels for large images manually is time consuming. We
introduce a partial loss-function and labeling strategies such that networks
can learn from partially interpreted seismic images. This strategy requires
only a small number of annotated pixels per seismic image. Tests on seismic
images and interpretation information from the Sea of Ireland show that we
obtain high-quality predicted interpretations from a small number of large
seismic images. The combination of a partial-loss function, a multi-resolution
network that explicitly takes small and large-scale geological features into
account, and new labeling strategies make neural networks a more practical tool
for automatic seismic interpretation.
