P/S wavefield separation using modified U-Net based on ConvNeXt architecture

Abstract

P/S wavefields separation is an essential step in multi-component seismic data processing, significantly impacting the quality of subsequent processing workflows if not performed accurately. Conventional wavefield separation methods, called model-driven methods, primarily rely on the polarization directions of P- and S-wavefields, which are obtained depending on accurate model parameters such as velocity and density. However, seismic model building is a well-known challenge for real data. As a result, the conventional methods rarely produce ideal pure P- or S-wavefields. Deep learning (DL) techniques can solve many real-world problems by constructing complex nonlinear mappings between inputs and desired outputs based on large-scale datasets, such as image classification, semantic segmentation, and object detection. Inspired by the advantages of DL techniques, we construct a improved U-ConvNeXt network architecture by combining the U-Net with the ConvNeXt, and apply it to implement P/S wavefields separation. Compared with the conventional U-Net, the ConvNeXt has better feature extraction capabilities because it introduces the idea of transformer architecture design into CNN. Consequently, the proposed U-ConvNeXt-based P/S wavefields separation method produces cleaner P- and S-wavefields with less computational costs. In addition, the proposed method can achieve nearly the same accuracy as the conventional methods without relying on model parameters, and its computational efficiency is much higher than conventional methods. Finally, we verify the proposed method with several numerical examples, and numerical results validate the effectiveness and accuracy of the proposed P/S wavefields separation method.