  The interpretation of seismic data is vital for characterizing sediments'
shape in areas of geological study. In seismic interpretation, deep learning
becomes useful for reducing the dependence on handcrafted facies segmentation
geometry and the time required to study geological areas. This work presents a
Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art
results for seismic facies segmentation. DNFS is trained using a combination of
cross-entropy and Jaccard loss functions. Our results show that DNFS obtains
highly detailed predictions for seismic facies segmentation using fewer
parameters than StNet and U-Net.
