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Liu G.,China University of Petroleum - Beijing | Chen X.,China University of Petroleum - Beijing | Du J.,Shengli Geophysical Research Institute | Wu K.,China University of Petroleum - Beijing
SEG Technical Program Expanded Abstracts | Year: 2011

We propose a novel method for random noise attenuation in seismic data by applying nonstationary autoregression (NAR) in frequency-space (f-x) domain. The method adaptively predicts the signal with special changes in dip or amplitude using f-x NAR. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional f-x deconvolution technique. The conventional f-x deconvolution uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic record. The proposed method does not require windowing strategies in spatial direction. We implement the algorithm by iterated scheme using conjugate gradient method. We constrain the coefficients to be smooth along space and frequency in f-x domain. The shaping regularization in least square inversion controls the smoothness of the coefficients of f-x NAR. There are two key parameters in the proposed method: filter length and radius of shaping operator. Synthetic and field data examples demonstrate that, compared with f-x deconvolution, f-x NAR can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure. © 2011 Society of Exploration Geophysicists.

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