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- W4385945405 abstract "Seismic data denoising via supervised deep learning is effective and popular but requires noise-free labels, which are rarely available. Blind-spot networks circumvent this requirement by training directly on noisy data and have been shown to be a powerful suppressor of random noise. In this work, we expand the methodology of blind-spot networks to create a blind-trace network that successfully removes trace-wise coherent noise. An extensive synthetic analysis illustrates the denoising procedures robustness to varying noise levels and varying numbers of noisy traces within shot gathers. It is shown that the network can accurately learn to suppress the noise when up to 60% of the original traces are noisy. Furthermore, the proposed procedure is implemented on the Stratton 3D field dataset and is shown to restore the previously corrupted direct arrivals. In addition to trace-wise noise suppression, we adapted the blind-spot networks to the successful suppression of colored Gaussian noise, which exhibits varying coherent properties in both time and spatial axes. The proposed adaptation of the blind-spot networks paves the way for its use in other applications, such as the suppression of coherent noise arising from wellsite activity, passing vessels, or nearby industrial activity." @default.
- W4385945405 created "2023-08-18" @default.
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- W4385945405 date "2023-08-17" @default.
- W4385945405 modified "2023-10-06" @default.
- W4385945405 title "Trace-wise coherent noise suppression via a self-supervised blind-trace deep learning scheme" @default.
- W4385945405 doi "https://doi.org/10.1190/geo2022-0371.1" @default.
- W4385945405 hasPublicationYear "2023" @default.
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