A sparse Bayesian joint inversion of multi-scale seismic data
Zhao Xiaolong1, Wu Guochen1,2, Cao Danping1,2
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 Sciences and Technology, Qingdao, Shandong 266071, China
Abstract:Joint inversion of multi-scale geophysical datasets is an effective way to improve accuracy and resolution of seismic inversion.Considering different confidence of data in different scale,we propose a sparse Bayesian joint inversion method of multi-scale seismic data.Firstly we obtain scale-constrained operator for different scale data through matching analysis for well-side synthetic seismogram with surface seismic data.Based on the Bayesian inversion framework,assuming that model parameters obey Cauchy prior distribution to retreive the sparse results,we derive a cost function for sparse Bayesian joint inversion for multi-scale seismic datasets with scale-constrained operator,and employ Polak-Ribiere-Polyak (PRP) conjugate gradient algorithm to solve the optimization problem.Model and real data tests show that the proposed inversion can highlight reservoir information from surface seismic data and borehole seismic data in high-confidence scale,and provide the high-accuracy inversion results for reservoir-oriented integrated investigation.
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