A sparse Bayesian joint inversion of multi-scale seismic data
(2.Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Sciences and Technology, Qingdao, Shandong, China 266071)
【Abstract】Joint inversion of multi-scale geophysical datasets is an effective way to improve accuracy and resolution of seismic inversion. Considering different confidences of data in different scales, 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 multiscale 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.
【Keywords】 sparse constraint; multi-scale seismic data; Bayesian theorem; joint inversion;
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