Simultaneous reconstruction and denoising for DAS-VSP seismic data by RRU-net - Tang, Huanhuan; Cheng, Shijun; Li, Wuqun; Mao, Weijian (Frontiers in Earth Science, 2023)
Distributed acoustic sensing in vertical seismic profile acquisition (DAS-VSP) plays an important role in reservoir monitoring. But the field data is noisy and associated with traces missing, which affects the seismic imaging and geological interpretation. Therefore, the DAS-VSP seismic data reconstruction with a high signal-to-noise ratio (SNR) is worth studying. There are no exact associations between signals and noise in the DAS-VSP seismic data, which means that reconstructing signal and suppressing noise simultaneously by the deep neural network are difficult. In this paper, we unify the reconstruction and denoising of DAS-VSP seismic data as a rank reduction problem of the high-rank matrix. Firstly, the training data set in t-x domain is transformed to texture maps in frequency domain through the Hankel matrix. The texture maps of incomplete data with noise are high- rank while that of complete data without noise are low-rank. Secondly, we established a rank reduction U-net (RRU-net), which consists of an encoding structure for down-sampling and a decoding structure for up-sampling. In the RRU-net, two-channel input/output layers are designed for the real and the imaginary parts of the texture map. At last, the RRU-net is trained to learn the mapping relationships between the high-rank texture maps and the low-rank texture maps to simultaneously reconstruct and denoise the DAS-VSP seismic data. Reconstruction data with high precision and high SNR could be obtained using our method. Meanwhile, presented reconstruction results of synthetic data and field data in this paper show the effectiveness of the RRU-net we proposed.