Abstract
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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This work was supported by the National Natural Science Foundation of China (41674141) and the Central Government Funds of Guiding Local Scientific and Technological Development under Grant number (2021ZYD0003).
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Gao, L., Zhao, K., Min, F. et al. Random noise suppression of seismic data through multi-scale residual dense network. Acta Geophys. 71, 637–647 (2023). https://doi.org/10.1007/s11600-022-00912-6
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DOI: https://doi.org/10.1007/s11600-022-00912-6