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• W4224096481 abstract "Distributed acoustic sensing (DAS), a new geophone for effective acquisition of vertical seismic profile, has several advantages, including low-cost, high-precision, and high temperature-resistance. However, it is susceptible to various noises that can contaminate the desired weak signals, thus denoising is an essential and independent procedure in DAS processing. Therefore, we propose the transform learning method to train a denoising model in the temporal-frequency domain considering that DAS noise is multiple and performs complexly in the time domain. Initially, synchrosqueezing transform is used to generate DAS data effective sparse representation in the frequency domain where the data remains more centered and uniform. Further, a redesigned convolutional neural network based on residual learning is built to extract different features between the noise and signal components, thus separating them. Finally, the denoised results are obtained by inversely transforming the denoised components into the time domain. Three factors improve the denoising performance of our method: 1. It can directly provide high-dimensional features for training, reducing data-dependency to an extent. 2. The designed network calculates a powerful nonlinear mapping between the additive noise and input noisy component, significantly reducing the training difficulties. 3. A new objective function in the synchrosqueezing frequency domain is designed for nonlinear mapping optimization. Both synthetic and real examples can demonstrate the method's effectiveness in denoising and improving the signal-to-noise ratio of DAS. Further, the denoised results can contribute to a more accurate velocity analysis." @default.
• W4224096481 created "2022-04-19" @default.
• W4224096481 creator A5044367029 @default.
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• W4224096481 date "2022-06-01" @default.
• W4224096481 modified "2023-09-30" @default.
• W4224096481 title "Transform learning in the synchrosqueezing frequency domain—A novel denoising strategy for optical fiber seismic records" @default.
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• W4224096481 doi "https://doi.org/10.1016/j.jappgeo.2022.104648" @default.
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