Seismic data interpolation via Unet with non-local blocks

Abstract

Due to the constraints of the exploration environment, acquired seismic data are commonly irregular or incomplete, which seriously affects the quality of subsequent seismic data processing and interpretation. Convolutional neural network (CNN)- based seismic data interpolation methods have been receiving increased attention owing to better efficiency and interpolation performance. UNet is a commonly used encoder-decoder network that uses skip connection, which can extract seismic data features using convolution filters. However, convolution filters are local operations that process a local neighbourhood and extract local features, but experience difficulty capturing global representations. Although an increase in network depth can expand the receptive field, the local operator still limits the precision of the recovered result. To address this limitation, we propose an improved UNet with a non-local block (NL-UNet), wherein non-local blocks are added to the classic UNet network. Owing to the addition of non-local blocks, NL-UNet can capture both local and non-local features of seismic data, which is conducive to improving its reconstruction performance. The applicability and effectiveness of NL-UNet were demonstrated through experiments on both synthetic and field datasets.