Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network - Wang, Detao; Chen, Guoxiong (Mathematical geosciences, 2021)
As an essential step in reservoir characterization, seismic stratigraphic interpretation is often dependent on the development of powerful computer-based interpretation tools that can simulate the intelligence of experienced interpreters. With the success of machine/deep learning applications in many aspects of geoscience in recent decades, geophysicists have become more dedicated to exploring seismic big data in a smarter and more sophisticated way to better image subsurface reservoirs/structures. In this paper, a specific U-shaped fully convolutional network (U-Net) is established for automatic seismic stratigraphic interpretation. Specifically, this task is formulated as a semantic segmentation problem by identifying strata at the pixel level and classifying each pixel in the image into a specific stratum category. An experiment using the Netherlands F3 seismic dataset suggests that, compared with previously established deep learning models requiring a large number of training sets, the proposed U-Net method can achieve high evaluation indicators and better stratum segmentations in the case of a limited training set. During the test, the proposed U-Net model outperforms the Bayesian neural network (BNN) model for seismic stratum segmentation with regard to the training time, prediction speed, and segmentation accuracy. These results indicate the great potential of using U-Net-based deep learning for intelligent seismic stratigraphic interpretation.