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, China 266580)
(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;

【DOI】

【Funds】 National Basic Research Program of China (973 Program) (2013CB228604) National Oil and Gas Major Project (2011ZX05030-004-002, 2011ZX05019-003) Shandong Provincial Natural Science Foundation, China (ZR2010DM016)

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This Article

ISSN:1000-7210

CN: 13-1095/TE

Vol 51, No. 06, Pages 1156-1163+1051

December 2016

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Article Outline

Abstract

  • 1 Introduction
  • 2 Scale-constrained operator
  • 3 Establishment of joint cost functional
  • 4 Model test
  • 5 Application in real data
  • 6 Conclusions
  • References