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Denoising Deep Learning Network Based on Singular Spectrum Analysis—DAS Seismic Data Denoising With Multichannel SVDDCNN

降噪 计算机科学 噪音(视频) 奇异谱分析 奇异值分解 噪声测量 模式识别(心理学) 奇异值 人工智能 特征向量 量子力学 图像(数学) 物理
作者
Qiankun Feng,Yue Li
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:60: 1-11 被引量:51
标识
DOI:10.1109/tgrs.2021.3071189
摘要

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious advantages over geophones, some weaknesses may limit its application. The main challenge is that DAS is polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. To suppress these novel noises, we developed a new denoising neural network based on singular spectrum analysis—multichannel singular value decomposition denoising convolutional neural network (SVDDCNN). The network can simultaneously extract data features from singular spectrum instead of the time domain, which can represent geophysical features more accurately and help separate signals from noises. Second, a multichannel input layer is designed, and the input is decomposed into three subspaces by singular spectrum analysis, which provides records of different signal-to-noise ratios (SNRs) for training and improves generalization ability of the network. Third, to enhance the quality of the data set, we added the noise subspace records removed by SVD into the training set to provide various forms of noise with different singular spectra. Both synthetic and field examples show that our network has achieved impressive denoising of DAS VSP and demonstrated competitive performance compared with other methods. Furthermore, the structure similarity (SSIM) map is introduced to evaluate the signal leakage by calculating the similarity between the denoised record and the removed noise record. The lowest SSIM index of the proposed network indicated superior signal preservation ability.
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