Random noise suppression of seismic data through multi-scale residual dense network

Random noise suppression is an important technique to improve the efficiency and accuracy of seismic data processing. Physical denoising methods such as f − x deconvolution and K-SVD have been widely adopted by the industry, while popular learning-based methods such as neural networks have emerged as good alternatives. In this paper, we propose a multi-scale residual dense network (MSRDN) for random noise suppression of seismic raw data. First, the network consists of a shallow feature extraction module, multiple residual blocks and multiple up-sampling modules. They are used for feature extraction, noise learning and size restoration. Second, each residual block is composed of multiple dense blocks. They are designed to alleviate network degradation. Third, dense blocks are tightly connected by multi-scale convolutional layers. They can enhance the regularization effect of the network. The experimental results show that MSRDN is more accurate and stable than previous algorithms.

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Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).