Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain
Li Fang1,2,3, Wang Shou-Dong1,2,3, Chen Xiao-Hong1,2,3, Liu Guo-Chang1,2,3, and Zheng Qiang4
1. State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing 102249, China.
2. CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Beijing 102249, China.
3. National Engineering Laboratory of Offshore Oil Exploration, China University of Petroleum, Beijing 102249, China.
4. CNOOC Research Institute, Beijing 100027, China.
Abstract：
The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace (RT) transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling (VSS) in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time (XT) domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.
LI Fang,WANG Shou-Dong,CHEN Xiao-Hong et al. Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain[J]. APPLIED GEOPHYSICS, 2013, 10(4): 423-432.
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2013, Vol. 10 
