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• W2904005001 endingPage "V350" @default.
• W2904005001 startingPage "V333" @default.
• W2904005001 abstract "Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the inputs are the raw data sets and the corresponding outputs are the desired clean data. After the completion of training, the deep-learning (DL) method achieves adaptive denoising with no requirements of (1) accurate modelings of the signal and noise or (2) optimal parameters tuning. We call this intelligent denoising. We have used a convolutional neural network (CNN) as the basic tool for DL. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with the acoustic wave equation. The stochastic gradient descent is used to solve the optimal parameters for the CNN. The runtime of DL on a graphics processing unit for denoising has the same order as the [Formula: see text]-[Formula: see text] deconvolution method. Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples." @default.
• W2904005001 created "2018-12-22" @default.
• W2904005001 creator A5068775552 @default.
• W2904005001 creator A5075889181 @default.
• W2904005001 creator A5078652354 @default.
• W2904005001 date "2019-11-01" @default.
• W2904005001 modified "2023-10-16" @default.
• W2904005001 title "Deep learning for denoising" @default.
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• W2904005001 doi "https://doi.org/10.1190/geo2018-0668.1" @default.
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