期刊论文详细信息
Acta Geophysica
Desert seismic noise suppression based on multimodal residual convolutional neural network
article
Wang, Shengnan1  Li, Yue1  Zhao, Yuxing1 
[1] Department of Information, College of Communication Engineering, Jilin University
关键词: Residual learning;    Desert seismic record;    Noise suppression;    Variational mode decomposition;   
DOI  :  10.1007/s11600-020-00405-4
学科分类:地球科学(综合)
来源: Polska Akademia Nauk * Instytut Geofizyki
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【 摘 要 】

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.

【 授权许可】

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