  One of the most crucial tasks in seismic reflection imaging is to identify
the salt bodies with high precision. Traditionally, this is accomplished by
visually picking the salt/sediment boundaries, which requires a great amount of
manual work and may introduce systematic bias. With recent progress of deep
learning algorithm and growing computational power, a great deal of efforts
have been made to replace human effort with machine power in salt body
interpretation. Currently, the method of Convolutional neural networks (CNN) is
revolutionizing the computer vision field and has been a hot topic in the image
analysis. In this paper, the benefits of CNN-based classification are
demonstrated by using a state-of-art network structure U-Net, along with the
residual learning framework ResNet, to delineate salt body with high precision.
Network adjustments, including the Exponential Linear Units (ELU) activation
function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold
cross-validation, have been deployed to further improve the prediction
accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows
good agreement between the predicted salt body and manually interpreted salt
body, especially in areas with weak reflections. This indicates the great
potential of applying CNN for salt-related interpretations.
