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Application of residual learning to microseismic random noise attenuation

Zheng Jing, Jiang Tianqi, Wu Zhixiang, Sun Yuan

Acta Geophysica

2021

Vol. 69, no. 4

1151--1161

Microseismic data which are recorded by near-surface sensors are usually drawn in strong random noise. The reliability and accuracy of arrivals picking, source localization, microseismic imaging and source mechanism inversion are often afected by the random noise. Random noise attenuation is important for microseismic data processing. We introduce a novel deep convolutional neural network-based denoising approach to attenuate random noise from 1D microseismic data. The approach predicts the noise (the diference between the noisy microseismic data and clean microseismic data) as output instead of directly outputing the denoised data that is called residual learning. With the residual learning strategy, the approach removes the clean data in the hidden layers. In other words, the approach learns from the random noise prior instead of an explicit data prior. Then, the denoised data are reconstructed via subtracting noise from noisy data. Compared with other commonly used denoising methods, the proposed method performs its efectiveness and superiority by experimental tests on synthetic and real data. The model is trained with synthetic data and applied on real data. The results show that random noise in the synthetic and real data can been removed. However, some noise still remains in the real data case. The reason for that may be the approach can only remove random noise nor the correlated noise. Other methods are needed to be applied to remove the correlated noise to obtain higher performance after that approach when the real microseismic data which contain both correlated noise and random noise.

Random noise attenuation using a structure oriented adaptive singular value decomposition

Xu Yankai, Cao Siyuan, Pan Xiao, Liu Wei, Chen Hongling

Acta Geophysica

2019

Vol. 67, no. 4

1091--1106

Singular value decomposition (SVD) is an efficient method to suppress random noise in seismic data. The performance of noise attenuation is typically affected by choosing the rank of the estimated signal using SVD. That the rank is fixed limits noise attenuation especially for a low signal-to-noise ratio data. Therefore, we propose a modified approach to attenuate random noise based on structure-oriented adaptively choosing singular values. In this approach, we first estimate dominant local slopes, predict other traces from a reference trace using the plane-wave prediction and construct a 3D seismic volume which is composed of all predicted traces. Then, we remove noise from a 2D profile whose traces are predicted from different reference traces via adaptive SVD filter (ASVD), which adaptively chooses the rank of estimated signal by the singular value increments. Finally, we stack every 2D denoised profile to a stacking denoised trace and reconstruct the 2D denoised seismic data which are composed of all stacking denoised traces. Synthetic data and field data examples demonstrate that the proposed structure-oriented ASVD approach performs well in random noise suppression for the low SNR seismic data with dipping and hyperbolic events.