Improved random noise attenuation using f–x empirical mode decomposition and local similarity
Gan Shu-Wei1, Wang Shou-Dong1, Chen Yang-Kang2, Chen Jiang-Long1, Zhong Wei1, and Zhang Cheng-Lin1
1. China University of Petroleum (Beijing), Beijing 102200, China.
2. The University of Texas at Austin, Austin TX, USA.
3. Research Institute of West-South Oil Company, PetroChina, Chengdu 610051, China.
Abstract Conventional f–x empirical mode decomposition (EMD) is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events. However, when a seismic event is not horizontal, the use of f–x EMD is harmful to most useful signals. Based on the framework of f–x EMD, this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals. Compared with conventional f–x EMD, f–x predictive filtering, and f–x empirical mode decomposition predictive filtering, the new approach can preserve more useful signals and obtain a relatively cleaner denoised image. Synthetic and field data examples are shown as test performances of the proposed approach, thereby verifying the effectiveness of this method.
This research is supported by the National Natural Science Foundation of China (No. 41274137) and the National Engineering Laboratory of Offshore Oil Exploration.
Cite this article:
Gan Shu-Wei,Wang Shou-Dong,Chen Yang-Kang et al. Improved random noise attenuation using f–x empirical mode decomposition and local similarity[J]. APPLIED GEOPHYSICS, 2016, 13(1): 127-134.
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2016, Vol. 13 
