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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression
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Deep Residual Encoder–Decoder Networks for Desert Seismic Noise Suppression

机译：用于沙漠地震噪声抑制的深度剩余编码器 - 解码器网络

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摘要

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.
机译：卷积神经网络（CNN）在许多领域取得了良好的性能，这引起了很多关注。 CNN是一种具有卷积计算和深度结构的前馈神经网络。在这封信中，针对中国沙漠地震勘探噪声的激烈干扰，提出了一种基于深度残余编码器解码器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，利用包含大量沙漠地震噪声的噪声集来训练网络，以便网络形成嘈杂记录和噪声之间的端到端映射。因此，通过从嘈杂记录中减去噪声来获得有效信号，以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，在合成记录和现场记录的处理中完全证明了所提出的方法的优点。特别是当信噪比（SNR）非常低时，这种提出的方​​法仍然可以具有非常好的去噪效果。

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