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A fully unsupervised and highly generalized deep learning approach for random noise suppression
Geophysical Prospecting ( IF 2.6 ) Pub Date : 2020-12-19 , DOI: 10.1111/1365-2478.13062
Omar M. Saad 1, 2 , Yangkang Chen 1
Affiliation  

In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features. Following this, the extracted features are decompressed to retrieve the seismic signal. Skip connections are used between the encoder and decoder parts, allowing the proposed algorithm to extract high‐order features without losing important information. Besides, dropout layers are used as regularization layers. The dropout layers preserve the most meaningful features belonging to the seismic signal and discard the remaining features. The proposed algorithm is an unsupervised approach that does not require prior information about the clean signal. The input patches are divided into 80% for training and 20% for testing. However, it is interesting to find that the proposed algorithm can be trained with only 30% of the input patches with an effective denoising performance. Four synthetic and four field examples are used to evaluate the proposed algorithm performance, and compared to the f x deconvolution and the f x singular spectrum analysis. The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.

中文翻译:

一种完全无监督且高度通用的深度学习方法,用于抑制随机噪声

在这项研究中,我们提出了一种深度学习算法(PATCHUNET),以抑制随机噪声并保留相干地震信号。输入数据分为几个补丁,每个补丁都经过编码以提取有意义的特征。此后,对提取的特征进行解压缩以检索地震信号。编码器和解码器部分之间使用跳过连接,从而使所提出的算法能够提取高阶特征而不会丢失重要信息。此外,辍学层用作正则化层。丢失层保留了属于地震信号的最有意义的特征,并丢弃了其余的特征。所提出的算法是一种无监督的方法,不需要关于干净信号的先验信息。输入色块分为80%用于训练和20%用于测试。然而,有趣的是发现所提出的算法可以仅用30%的输入色块进行训练,并具有有效的降噪性能。使用四个综合示例和四个现场示例来评估所提出的算法性能,并将其与 F - X 去卷积和 F - X 奇异频谱分析。结果表明,尽管存在大量随机噪声,例如当输入信噪比低至-14.2 dB时,该算法仍具有衰减随机噪声和有效保留地震信号的能力。 。
更新日期:2020-12-19
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