Reviving Legacy Seismic Data via Machine Learning Technique—Part 1: Expanding 3-D Seismic Survey Coverage With Gated Convolution GAN

We propose a novel machine learning-based seismic volume reconstruction method that gradually extrapolates a seed volume by referring to line data external to the seed volume. The proposed method employs the generative adversarial network (GAN) framework with gated convolution, facilitating the training process by providing feedback for the extrapolated volumes. Our approach can be applied to address the practical limitations associated with the shortage of 3-D data often encountered in seismic surveys, where 2-D data typically cover regional areas, but 3–D data are confined to small areas. To alleviate the scarcity and limited availability of seismic survey data, we introduce effective data augmentation methods that ensure the robustness and generality of our neural network even when trained with only a seed volume. Unlike a simple supervised scheme, our approach (i.e., adversarial scheme) employs an auxiliary network during the extrapolation process, which functions similarly to the discriminator in the GAN framework, improving the network’s performance for restoring strata and high-frequency features. Through numerical examples, spectral analyses, and performance evaluations for extrapolated volumes, we demonstrate that our method integrates 2-D and 3-D seismic data more effectively than the supervised scheme to produce extensive volumes. Furthermore, our method demonstrates relatively robust performance in cases where input lines show high or low consistency to each other although some artifacts are observed in the low-consistency case.