3D seismic random noise suppression with sparse and redundant representation
Zhang Guangzhi1, Chang Dekuan2, Wang Yihui3, Li Zhenzhen1, Zhao Yang1, Yin Xingyao1
1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China;
2. Northwest Branch of Petroleum Research Institute of Exploration and Development, PetroChina, Lanzhou, Gansu 730020, China;
3. Geophysical Exploration Research Institute, Huabei Oilfield company, PetroChina, Renqiu, Hebei 062552, China
Abstract：Conventional methods to suppress random noise work very well for 2D seismic data, but not for 3D seismic data. Therefore we present in this paper a new method to remove random noise from 3D seismic data, which is driven by sparse and redundant representation algorithm. Under Bayesian framework, this method uses 3D overcomplete DCT dictionary to sparsely and redundantly represent seismic data. Using orthogonal matching pursuit (OMP) and K-singular value decomposition (K-SVD) continuously to update 3D sparse matrix and 3D overcomplete DCT dictionary, random noise of 3D seismic data can be significantly suppressed. We apply this proposed method to both 3D theoretical and real seismic data together with conventional f-x deconvolution method and K-L transform method. Application results show that the proposed method can improve signal to noise ratio and protect seismic signals, and slice continuity and smoothness, and the resolution of complicated structures are also improved.
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