Seismic random noise suppression using improved CycleGAN
نویسندگان
چکیده
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in surface conditions and media. The primary challenges related to processing data have always been reducing random increasing ratio. In this study, we propose an improved cycle-consistent generative adversarial network (CycleGAN) suppression method. First, generator replaces original structure with Unet combined Resnet order increase diversity feature extraction decrease loss details. Second, improve network’s stability, effect, event texture preservation ratio, Least Square GAN (LSGAN) square difference is used place conventional cross-entropy loss. feasibility proposed method was confirmed using model real data, both which demonstrated that effectively suppressed data. addition, denoising effect superior widely FX deconvolution
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Contents of this paper were reviewed by the Technical Committee of the 9 th International Congress of the Brazilian Geo-physical Society. Ideas and concepts of the text are the authors' responsibility and do not necessarily represent any position of the SBGf, its officers or members. Electronic reproduction or storage of any part of this paper for commercial purposes withou the written consent ...
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ژورنال
عنوان ژورنال: Frontiers in Earth Science
سال: 2023
ISSN: ['2296-6463']
DOI: https://doi.org/10.3389/feart.2023.1102656