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《应用地球物理(英文版)》 2011-04
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Seismic data denoising based on mixed time-frequency methods

Cai Han-Peng 2 , He Zhen-Hua 1,2 , and Huang De-Ji 21. State Key Lab of Oil and Gas Reservoir Geology and Exploitation of Chengdu University of Technology, Chengdu 610059, China. 2. Geophysics Institution of Chengdu University of Technology, Chengdu 610059, China.  
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain (t-f-x). Then in the t-f-x domain apply Empirical Mode Decomposition (EMD) on each frequency slice and clear the Intrinsic Mode Functions (IMFs) that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.
【Fund】: sponsored by the National Natural Science Foundation of China (Grant No. 41174114);; the National Natural Science Foundation of China and China Petroleum & Chemical Corporation Co-funded Project (No. 40839905)
【CateGory Index】: P631.4
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