FDSANet: Seismic Data Reconstruction Based on a Frequency-Domain Self-Attention Network

Abstract

The seismic data reconstruction is a crucial step in seismic data processing. Most existing methods reconstruct seismic data in the spatial domain, often ignoring some important frequency components in the frequency domain, such as high-frequency texture features. Therefore, we propose a frequency-domain self-attention network (FDSANet) to effectively reconstruct seismic data with a high missing rate. The wavelet transform is employed in this model to better restore weak signals and provide more information at different resolutions. The fast Fourier transform in the frequency-domain self-attention module (FDSAM) enhances the global frequency awareness, especially for high-frequency energy. Different frequency components are elementwise multiplied by dynamic weights, effectively suppressing energy leakage and aliasing. Moreover, the nearest neighbor similarity loss on adjacent shot gathers is incorporated into the loss function to learn information from neighboring shot gathers, further enhancing the reconstruction performance of our model. Experiments on both synthetic and field datasets demonstrate that FDSANet achieves significant improvement over several state-of-the-art methods.