2019 Vol. 62, No. 1
Article Contents

WANG YuQing, LU WenKai, LIU JinLin, ZHANG Meng, MIAO YongKang. 2019. Random seismic noise attenuation based on data augmentation and CNN. Chinese Journal of Geophysics (in Chinese), 62(1): 421-433, doi: 10.6038/cjg2019M0385
Citation: WANG YuQing, LU WenKai, LIU JinLin, ZHANG Meng, MIAO YongKang. 2019. Random seismic noise attenuation based on data augmentation and CNN. Chinese Journal of Geophysics (in Chinese), 62(1): 421-433, doi: 10.6038/cjg2019M0385

Random seismic noise attenuation based on data augmentation and CNN

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  • Convolutional neural network (CNN) has been widely adopted in various research fields of computer science. Combining the process of feature extracting and classification, CNN greatly simplifies traditional data processing task. However, as a data-driven algorithm, the generalization ability of CNN is limited in the problem of seismic noise attenuation which lacks labeled data. To solve this problem, we propose a CNN training framework based on data generation and augmentation for seismic noise attenuation. When processing synthetic data, we add Gaussian noise with different variance levels to clean seismic data and further augment training datasets to increase the diversity of features. For real seismic data, the clean data and corresponding noise are hard to acquire, thus we propose a method to generate labeled datasets directly from unlabeled noisy seismic data. In the proposed method, we apply existing denoising method to obtain the estimated clean data and estimated noise from real seismic data. The estimated data retains similar texture characteristics with clean data and the estimated noise has similar probability distribution with real seismic noise. We compare our method with F-X deconvolution, BM3D and adaptive frequency domain filtering method. The experiment results demonstrate that our method can efficiently attenuate random noise while preserving signals. Finally, we adopt neural network visualization methods to our CNN model and the visualization results explain the texture patterns learned by each layer of our network to some extent.

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