Diverse Noise Suppression Based on SKUformer for DAS VSP Data

Seismic noise suppression plays a crucial role in seismic data processing and geological structure interpretation. Distributed acoustic sensing (DAS) is widely applied in acquisition of vertical seismic profiling (VSP) data, but the collected seismic data often contains various complex noise with strong energy such as background noise, single-trace low-frequency interference, single-trace random interference, and coupled noise. Therefore, how to effectively suppress the diverse noise for DAS VSP data is important in subsequent seismic data processing. At present, deep learning methods based on convolutional neural network (CNN) have been extensively employed in seismic denoising and have achieved remarkable results, but they often exhibit poor ability to capture global information. Subsequently, deep learning methods based on Transformer are developed, which excel at capturing global features but exhibit high computational complexity and weak ability to capture local information. However, extracting both global and local features is crucial for seismic noise suppression. Global features provide insight into the overall structure of seismic data, while local features capture detail features containing rich geological structure information. Therefore, we propose a deep learning approach based on selective kernel feature fusion Uformer (SKUformer), which combines the advantages of Transformer and CNN to simultaneously capture global and local information. First, we replace the convolutional layer in U-net with the parallel dilated convolution locally enhanced shifted window (PDC-LeSwin) Transformer block, enhancing the capability of network to capture global information and acquiring multiscale information for VSP data. Moreover, we introduce the PDC module and locally enhanced feed-forward (LeFF) module to extract rich detailed information in VSP data. Additionally, we change the skip connection to the selective kernel feature fusion (SKFF) module to selectively fuse multiscale features, including low-frequency features and detailed features in VSP data. Finally, we validate the effectiveness of our method by applying it to the synthetic and the field DAS VSP data, comparing it with three other methods, thus demonstrating superiority in removing complex noise while preserving effective seismic signals.