Journal of Jilin University (Information Science Edition) ›› 2025, Vol. 43 ›› Issue (3): 489-496.
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WANG Ruimin, YANG Wenbo, DENG Cong, LU Tongxiang, ZHANG Wenxiang
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Abstract: Due to the limitation of environmental and economic factors, the collected seismic records usually have a lot of noise interference, which may cause some obstacles to the subsequent seismic data processing. Effectively attenuating noise is a key issue in seismology. In recent years, CNNs ( Convolutional Neural Networks) have achieved some success in the field of seismic data denoising. However, weak signal recovery in the presence of strong background noise is insufficient for existing convolutional neural networks. To address the above issues, a denoising network called MAUnet(Multi-Scale U-Net and An Attention Fusion Mechanism) is proposed. based on a multi-scale U-Net and an attention fusion mechanism. MAUnet innovatively introduces a dual-mechanism architecture, where a multi-scale module enables the network to learn features at different scales. And an attention-based feature fusion mechanism allows the network to combine shallow high-frequency details with deep semantic information, enhancing its learning capability and achieving feature complementarity. Experimental results demonstrate that our method has better noise attenuation and recovery capability for weak signals than competitive methods.
Key words: convolutional neural network (CNN), seismic data denoising, multi-scale feature extraction, attention feature fusion
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WANG Ruimin, YANG Wenbo, DENG Cong, LU Tongxiang, ZHANG Wenxiang . Seismic Denoising Method of Multiscale and Attentional Feature Fusion[J].Journal of Jilin University (Information Science Edition), 2025, 43(3): 489-496.
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http://xuebao.jlu.edu.cn/xxb/EN/Y2025/V43/I3/489
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