Multi-dimensional pre-stack seismic inversion based on sparse representation
YANG Sen1, WU Guochen1,2, ZHANG Mingzhen3, DU Zeyuan1, SHAN Junzhen1, LIANG Zhanyuan1
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266580, China; 3. Shengli Geophysical Research Institute of SINO-PEC, Dongying, Shandong 257022, China
Abstract：It is difficult to characterize thin reservoirs through conventional pre-stack and post-stack inversion.Interference effects generated by stacking may fade,distort,and suppress effective signals; thus,we suggest avoiding stacking to preserve signals.Pre-stack attributes vary with offset and frequency; this means that FAVO inversion based on high-resolution spectral decomposition may be capable of addressing the issue of fluid detection.We present multi-dimensional pre-stack seismic inversion based on sparse representation,and the point is no stacking.The first step is to extract angle gathers at the zone of interest and then perform high-resolution time-frequency decomposition for each single angle gather extracted in terms of sparse representation.The second step is to establish the mapping relationship between impedance and seismic data in accordance with the Bayesian theory and add some perturbation,calculated using non-linear optimization,to the initial model.The last step is to take the result at the previous frequency as the constraint to perform inversion at the next frequency until the final output at each angle is obtained.The case study in Prospect A shows that constrained inversion at each angle and frequency generates multi-dimensional results with high precision; this facilitates thin reservoir characterization.
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