Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression
Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression
复制标题
用于沙漠地震噪声抑制的深度残留编码器和解码器网络
DOI：
10.1109/lgrs.2019.2925062
复制
发表时间：
2020-03
影响因子：
4.8
通讯作者：
Hongzhou Wang
中科院分区：
文献类型：
--
作者：
Haitao Ma;Haiyang Yao;Yue Li;Hongzhou Wang
卷积神经网络（CNN）在许多领域都取得了优异的性能，备受关注。CNN是一种具有卷积计算和深度结构的前向神经网络。针对我国沙漠地区地震勘探噪声干扰强烈的问题，提出了一种基于深层残差编解码网络的沙漠地震降噪系统。为了提取沙漠地震噪声的特征和变化规律，利用含有大量沙漠地震噪声的噪声集对网络进行训练，使网络形成带噪记录与噪声的端到端映射。因此，通过从噪声记录中减去噪声来获得有效信号，从而实现令人满意的去噪性能。与传统的随机噪声抑制方法相比，该方法在合成记录和野外记录的处理中充分体现了其优越性。特别是在信噪比很低的情况下，该方法仍能有很好的去噪效果。
The convolutional neural network (CNN) has achieved excellent performance in many fields, which has attracted much attention. CNN is a kind of feedforward neural network with convolution computation and depth structure. In this letter, aiming at the intense interference of seismic exploration noise in the desert of China, a desert seismic noise reduction system based on deep residual encoder–decoder network is proposed. In order to extract the characteristics and variation law of desert seismic noise, a noise set containing a large number of desert seismic noise is utilized for training the network so that the network forms the end-to-end mapping between the noisy records and the noise. Consequently, the effective signals are obtained by subtracting noise from the noisy records so as to achieve a satisfactory denoising performance. Compared with the traditional random noise suppression methods, the advantages of the proposed method are fully demonstrated in the processing of the synthetic records and the field records. Especially when the signal-to-noise ratio (SNR) is very low, this proposed method can still have a very good denoising effect.
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影响因子：
0.7
作者：
S. Cao;Xiang-tao Chen
通讯作者：
S. Cao;Xiang-tao Chen
影响因子：
8.2
作者：
Guanghui Li;Yue Li;Baojun Yang
通讯作者：
Guanghui Li;Yue Li;Baojun Yang
用于地震随机噪声衰减的抛物线迹时频峰值滤波
影响因子：
4.8
作者：
Tian, Yanan;Li, Yue
通讯作者：
Li, Yue
超越高斯降噪器：用于图像降噪的深度 CNN 残差学习
影响因子：
10.6
作者：
Zhang, Kai;Zuo, Wangmeng;Zhang, Lei
通讯作者：
Zhang, Lei
DOI：
--
发表时间：
2016-03
期刊：
--
影响因子：
--
作者：
Xiao-Jiao Mao;Chunhua Shen;Yubin Yang
通讯作者：
Xiao-Jiao Mao;Chunhua Shen;Yubin Yang