Sparse seismic data regularization in shot and trace domains using a residual block autoencoder based on the fast Fourier transform

Abstract

The increasing use of sparse acquisitions in seismic data acquisition offers advantages in cost and time savings. However, it results in irregularly sampled seismic data, adversely impacting the quality of the final images. In this paper, we develop the residual block fast Fourier transform-convolutional autoencoder (ResFFT-CAE) network, a convolutional neural network with residual blocks based on the Fourier transform. Incorporating residual blocks allows the network to extract high- and low-frequency features from seismic data. The high-frequency features capture detailed information, whereas the low-frequency features integrate the overall data structure, facilitating superior recovery of irregularly sampled seismic data in the trace and shot domains. We evaluate the performance of the ResFFT-CAE network on the synthetic and field data. On synthetic data, we compare the ResFFT-CAE network with the compressive sensing method using the curvelet transform. On field data, we conduct comparisons with other neural networks, such as the CAE and U-Net. The results demonstrate that the ResFFT-CAE network consistently outperforms other approaches in all scenarios. It produces images of superior quality, characterized by lower residuals and reduced distortions. Furthermore, when evaluating model generalization, tests using models trained on synthetic data also exhibit promising results. In conclusion, the ResFFT-CAE network indicates great promise as a highly efficient tool for regularizing irregularly sampled seismic data. Its excellent performance suggests potential applications in the preconditioning of seismic data analysis and processing flows.