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- W4387331374 abstract "Strong energy noise is one of the typical interferences in seismic exploration, which is usually attenuated with custom threshold methods. The key to ideal denoising is to accurately identify the zones of strong energy noise in seismic records and then determine the reasonable denoising thresholds. However, due to the influence of surface and acquisition conditions, the distribution and characteristics of strong energy noise in seismic records vary greatly and constantly change over time and space. In practice, repeated experiments are required to determine the noise zones and reasonable thresholds. In this study, we propose a new method that utilizes U-Net deep learning to automatically identify strong energy noise in seismic data. Our trained network could accurately identify the intrinsic characteristics of strong energy interference in seismic data and adapt to the spatiotemporal variability of noise, thus facilitating more efficient and accurate determination of denoising thresholds. The method transformed the identification of strong energy noise into a binary segmentation task of the image and solved it by U-Net. The practical applications validated that this approach significantly improved the accuracy of strong energy denoising under complex near-surface conditions, while reducing the time and labor cost of analyzing and testing denoising parameters, and markedly improving efficiency." @default.
- W4387331374 created "2023-10-05" @default.
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- W4387331374 date "2023-01-01" @default.
- W4387331374 modified "2023-10-05" @default.
- W4387331374 title "Automatic Identification of Strong Energy Noise in Seismic Data Based on U-Net" @default.
- W4387331374 doi "https://doi.org/10.1109/tgrs.2023.3321898" @default.
- W4387331374 hasPublicationYear "2023" @default.
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