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- Title
Seismic Shot Gather Denoising by Using a Supervised-Deep-Learning Method with Weak Dependence on Real Noise Data: A Solution to the Lack of Real Noise Data.
- Authors
Dong, Xintong; Lin, Jun; Lu, Shaoping; Huang, Xingguo; Wang, Hongzhou; Li, Yue
- Abstract
In recent years, supervised-deep-learning methods have shown some advantages over conventional methods in seismic data denoising, such as higher signal-to-noise ratio after denoising, complete separation of signals and noise in shared frequency bands and intelligent denoising without artificial parameter tuning. However, the lack of real noise data matched with raw seismic data has greatly limited its further application. In this paper, we take the surface seismic shot gather as an example to explore the corresponding solutions and propose a novel supervised-deep-learning method with weak dependence on real noise data based on the data augmentation of a generative adversarial network. We utilize the generative adversarial network to augment the pre-arrival noise data acquired from the shot gather itself, thereby obtaining a large amount of synthetic noise data whose probability distribution is extremely similar to that of the real noise in shot gather; the augmented synthetic noise data and sufficient synthetic signal data obtained by forward modeling together form the augmented training dataset. Meanwhile, the dilated convolution and gradual denoising strategy are adopted to construct the basic architecture of denoising convolution neural network. Finally, the above augmented dataset is used to train the network, so as to establish a nonlinear and complex mapping relationship between raw seismic data and desired signals. Both synthetic and real experiments demonstrate that our method can realize the intelligent denoising of different common-shot-point records in shot gather with the help of limited pre-arrival noise data. Article Highlights: We introduce the data augmentation strategy into the field of deep-learning-based seismic denoising, thereby alleviating the dependence of supervised-deep-learning methods on real noise data We propose a novel denoising network architecture with strong recovery ability for weak desired signals by using the gradual denoising strategy and dilated convolution The augmented synthetic noise data can meet the requirement of supervised-deep-learning methods on the quantity and authenticity of training data, so this data augmentation strategy by using the Generative Adversarial Net (GAN) is a solution to the lack of real noise data
- Subjects
RANDOM noise theory; MICROSEISMS; GENERATIVE adversarial networks; CONVOLUTIONAL neural networks; DISTRIBUTION (Probability theory); DATA augmentation; NOISE
- Publication
Surveys in Geophysics, 2022, Vol 43, Issue 5, p1363
- ISSN
0169-3298
- Publication type
Article
- DOI
10.1007/s10712-022-09702-7