  Delimiting salt inclusions from migrated images is a time-consuming activity
that relies on highly human-curated analysis and is subject to interpretation
errors or limitations of the methods available. We propose to use migrated
images produced from an inaccurate velocity model (with a reasonable
approximation of sediment velocity, but without salt inclusions) to predict the
correct salt inclusions shape using a Convolutional Neural Network (CNN). Our
approach relies on subsurface Common Image Gathers to focus the sediments'
reflections around the zero offset and to spread the energy of salt reflections
over large offsets. Using synthetic data, we trained a U-Net to use
common-offset subsurface images as input channels for the CNN and the correct
salt-masks as network output. The network learned to predict the salt
inclusions masks with high accuracy; moreover, it also performed well when
applied to synthetic benchmark data sets that were not previously introduced.
Our training process tuned the U-Net to successfully learn the shape of complex
salt bodies from partially focused subsurface offset images.
