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Zhao, Y X, Li, Y, Yang, B J (2020) Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network. Geophysical Journal International, 221 (2) 1211-1225 doi:10.1093/gji/ggaa071

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Reference TypeJournal (article/letter/editorial)
TitleDenoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network
JournalGeophysical Journal International
AuthorsZhao, Y XAuthor
Li, YAuthor
Yang, B JAuthor
Year2020 (May 1)Volume221
Issue2
PublisherOxford University Press (OUP)
DOIdoi:10.1093/gji/ggaa071Search in ResearchGate
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Mindat Ref. ID892777Long-form Identifiermindat:1:5:892777:8
GUID0
Full ReferenceZhao, Y X, Li, Y, Yang, B J (2020) Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network. Geophysical Journal International, 221 (2) 1211-1225 doi:10.1093/gji/ggaa071
Plain TextZhao, Y X, Li, Y, Yang, B J (2020) Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network. Geophysical Journal International, 221 (2) 1211-1225 doi:10.1093/gji/ggaa071
In(2020, May) Geophysical Journal International Vol. 221 (2) Oxford University Press (OUP)

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Zhao, Y X, Li, Y, Wu, N (2021) Data augmentation and its application in distributed acoustic sensing data denoising. Geophysical Journal International, 228 (1) 119-133 doi:10.1093/gji/ggab345
Zhao, Y X, Li, Y, Wu, N, Wang, S N (2022) Sample2Sample: an improved self-supervised denoising framework for random noise suppression in distributed acoustic sensing vertical seismic profile data. Geophysical Journal International, 232 (3) 1515-1532 doi:10.1093/gji/ggac400
Ma, Haitao, Wang, Yuzhuo, Li, Yue, Zhao, Yuxing (2022) Desert Seismic Low-Frequency Noise Attenuation Using Low-Rank Decomposition-Based Denoising Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 60. 1-9 doi:10.1109/tgrs.2021.3055506
Li, Yue, Li, Linlin, Zhang, Chao (2019) Desert seismic signal denoising by 2D compact variational mode decomposition. Journal of Geophysics and Engineering, 16. 1048-1060 doi:10.1093/jge/gxz065
Dong, X T, Li, Y, Yang, B J (2019) Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic. Geophysical Journal International, 219 (2) 1281-1299 doi:10.1093/gji/ggz363
Zhao, Yuxing, Li, Yue, Yang, Baojun (2020) Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 58 (1) 650-665 doi:10.1109/tgrs.2019.2938836
Wang, Benfeng, Wu, Ru-Shan, Chen, Xiaohong, Li, Jingye (2015) Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform. Geophysical Journal International, 201 (2) 1182-1194 doi:10.1093/gji/ggv072
Moghadas, Davood (2020) One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network. Geophysical Journal International, 222 (1) 247-259 doi:10.1093/gji/ggaa161
Viens, Loïc, Van Houtte, Chris (2020) Denoising ambient seismic field correlation functions with convolutional autoencoders. Geophysical Journal International, 220 (3) 1521-1535 doi:10.1093/gji/ggz509
Wang, Shengnan, Li, Yue, Zhao, Yuxing (2020) Desert seismic noise suppression based on multimodal residual convolutional neural network. Acta Geophysica, 68 (2) 389-401 doi:10.1007/s11600-020-00405-4
Moretti, L, Mangeney, A, Walter, F, Capdeville, Y, Bodin, T, Stutzmann, E, Le Friant, A (2020) Constraining landslide characteristics with Bayesian inversion of field and seismic data. Geophysical Journal International, 221 (2) 1341-1348 doi:10.1093/gji/ggaa056
 
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