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 (DAS-VSP) acquisition plays an important role in reservoir monitoring. But the field data can be noisy and associated with missing traces 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 relationships between signals and noise in the t-x domain DAS-VSP seismic data, which means that reconstructing signals and suppressing noise simultaneously by the deep neural network is difficult. We develop a novel algorithm based on U-net in combination with the Hankel matrix as input/output, rather than t-x domain seismic data. The frequency domain Hankel matrix of the seismic data is proposed to facilitate the reconstruction and denoising of DAS-VSP seismic data as a rank reduction problem of the high-rank matrix. The Hankel matrices of incomplete data with noise are high-rank ones while those of complete data without noise are low-rank ones, which is beneficial to the network learning. In our proposed rank reduction U-net (RRU-net), two-channel input/output layers are designed for the real and the imaginary parts of the Hankel matrix in the frequency domain. Thus, reconstructed data with high precision and high SNR could be obtained using a trained RRU-net. Meanwhile, we tested our RRU-net algorithm on two synthetic data and one field data, and the results show the effectiveness and the feasibility of the method. Our algorithm performs better than both the U-net-based method that uses t−x domain data as input/output and the rank reduction approach.