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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ژورنال

عنوان ژورنال: Frontiers in Earth Science

سال: 2023

ISSN: ['2296-6463']

DOI: https://doi.org/10.3389/feart.2023.1102656