Seismic data reconstruction based on a multicascade self-guided network

Due to inherent limitations in data acquisition, seismic data reconstruction is an important procedure to recover missing data or improve observation density. Many conventional methods exist to solve the reconstruction task. Reconstruction is challenging, especially in the case of complex seismic data. Recently, convolutional neural networks (CNN) have been applied in seismic data processing. In most cases, the architectures of these CNN-based methods are relatively simple, without sufficient feature interaction, limiting their performance. To improve reconstruction results, a multicascade self-guided network (MSG-Net) is presented. In general, MSG-Net is inspired by the self-guided scheme, and a multicascade architecture is designed to extract informative features within the analyzed seismic data at different resolutions. Following this, a parallel spatial attention module is used to further refine and enhance the primary features, thereby improving reconstruction accuracy. To test and verify the new approach, a training data set is generated, based on the synthetic records obtained by forward modeling methods. Experimental results demonstrate that MSG-Net is a promising approach for performing seismic data interpolation.