Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks

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. Our network is trained using a combination of cross-entropy and Jaccard loss functions. The results show that DNFS with fewer parameters than StNet and U-Net, trained with a composite loss function and a dataset for binary-segmentation in a few minutes, can offer high detailed predictions for seismic facies segmentation.