Fully Connected U-Net and Its Application on Reconstructing Successively Sampled Seismic Data

One of the major hot topics in seismic data processing is the reconstruction of successively sampled seismic data. There are numerous traditional methods proposed for addressing this issue; however, they still have unavoidable drawbacks, such as high computational cost and sensitive tuning parameters. In this study, we suggest a deep learning model for reconstructing successively sampled seismic data, termed fully connected U-Net (FCU-Net). FCU-Net maintains the high-resolution representations by connecting the parallel different-resolution representations and repeating multi-scale fusion. Such a structure allows FCU-Net to successfully extract multi-scale information, which is beneficial for accurate seismic data reconstruction. Additionally, the extending subnetwork of FCU-Net contains a large number of feature channels and sufficient information interaction between different resolution representations via the composite cascades, which contributes to locating successively sampled traces with big gaps and then performing the seismic interpolation. To verify the effectiveness of FCU-Net, we compare it with state-of-the-art networks, i.e., U-Net and HRNet, using synthetic and field examples. The results show that FCU-Net performs best when interpolating successively sampled seismic data, proving its superiority and availability.