Unsupervised Erratic Seismic Noise Attenuation With Robust Deep Convolutional Autoencoders

Erratic seismic noise, following a (known or unknown) non-Gaussian distribution, poses a formidable challenge to conventional methods of random noise attenuation. Many erratic noise cancellation methods, for instance, robust reduced-rank and sparsity-promoting filtering, have been proven to achieve...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 16
Hauptverfasser:	Qian, Feng, Guo, Wei, Liu, Zhangbo, Yu, Hongtao, Zhang, Gulan, Hu, Guangmin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Attenuation Back propagation Back propagation networks Deep learning Deep learning (DL) erratic seismic noise Gaussian distribution Image denoising Machine learning Methods Neural networks Noise Noise measurement Noise reduction Normal distribution Random noise robust deep convolutional autoencoder (RDCAE) Robustness total variation (TV) Training Transforms Welsch function
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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