Desert seismic noise suppression based on multimodal residual convolutional neural network
Desert seismic noise suppression based on multimodal residual convolutional neural network
复制标题
基于多模态残差卷积神经网络的沙漠地震噪声抑制
DOI：
10.1007/s11600-020-00405-4
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发表时间：
2020-02
期刊：
影响因子：
2.3
通讯作者：
Yuxing Zhao
中科院分区：
文献类型：
--
作者：
Shengnan Wang;Yue Li;Yuxing Zhao
Seismic exploration is an important means of oil and gas detection, but affected by complex surface and near-surface conditions, and the seismic records are polluted by noise seriously. Particularly in the desert areas, due to the influence of wind and human activities, the complex desert noise with low-frequency, nonstationary and non-Gaussian characteristics is produced. It is difficult to extract effective signals from strong noise using existing denoising methods. To address this issue, the paper proposes a new denoising method, called multimodal residual convolutional neural network (MRCNN). MRCNN combines convolutional neural network (CNN) with variational modal decomposition (VMD) and adopts residual learning method to suppress desert noise. Since CNN-based denoisers can extract data features based on massive training set, the impact of noise types and intensity on the denoised results can be ignored. In addition, VMD algorithm can sparsely decompose signal, which will facilitate the feature extraction of CNN. Therefore, using VMD algorithm to optimize the input data will conducive to the performance of the network denoising. Moreover, MRCNN adopts reversible downsampling operator to improve running speed, achieving a good trade-off between denoising results and efficiency. Extensive experiments on synthetic and real noisy records are conducted to evaluate MRCNN in comparison with existing denoisers. The extensive experiments demonstrate that the MRCNN can exhibit good effectiveness in seismic denoising tasks.
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影响因子：
4.8
作者：
S. Yuan;Shangxu Wang;C. Luo;Tieyi Wang
通讯作者：
S. Yuan;Shangxu Wang;C. Luo;Tieyi Wang
影响因子：
3.3
作者：
G. Pilikos;Anita C. Faul
通讯作者：
G. Pilikos;Anita C. Faul
结构化图字典学习及其在地震去噪中的应用
影响因子：
8.2
作者：
Liu Lina;Ma Jianwei
通讯作者：
Ma Jianwei
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
--
作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
--
作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi