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;


【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)

Download this article


    [1]Yang Wencai. Theory and methods of geophyiscal inversion. Geological Publishing House, Beijing, 1997 (in Chinese).

    [2]Sa Liming, Yang Wuyang, Yao Fengchang, et al. Past, present, and future of geophysical inversion. OGP, 2015, 50 (1): 184–202 (in Chinese).

    [3]Tian Jun, Wu Guochen, Zong Zhaoyun. Robust three-term AVO inversion and uncertainty analysis. OGP, 2013, 48 (3): 443–449 (in Chinese).

    [4]Yuan S, Wang S. Spectral sparse Bayesian learning reflectivity inversion. Geophysical Prospecting, 2013, 61 (4): 735–746.

    [5]Liu Xiaojing, Yin Xingyao, Wu Guochen, et al. Prestack seismic inversion based on orthogonal matching pursuit algorithm. OGP, 2015, 50 (5): 925–935 (in Chinese).

    [6]He Weihui, Wang Jialin, Yu Peng. Overview of the status and prospect of geophysical joint inversion. Progress in Geophysics, 2009, 24 (2): 530–540 (in Chinese).

    [7]Cao Danping, Yin Xingyao, Zhang Fanchang et al. Astudy on the method of joint inversion of multiscale seismic data. Chinese Journal of Geophysics, 2009, 52 (4): 1 059–1 067 (in Chinese).

    [8]Wang Cheng, Pei Jiangyun, Wang Lina, et al. Crosshole seismic waveform tomography in Changyuan Oilfield, Daqing. OGP, 2014, 49 (5): 904–910 (in Chinese).

    [9]Li Jianhua, Liu Baihong, Zhang Yanqing et al. Method for fine-description of reservoir based on cross-hole seismic data. OGP, 2008, 43 (1): 41–47 (in Chinese).

    [10]Leiceaga G G, Marion B, O’Sullivan K M, et al. Crosswell seismic applications for improved reservoir understanding. The Leading Edge, 2015, 34 (4): 422–428.

    [11]Ibrahim M S, Pennington W D, Turpening R M. Crosswell seismic imaging of acoustic and shear impedance in a Michigan reef. The Leading Edge, 2010, 29 (6): 706–711.

    [12]Zhao X, Wu G, Yin X, et al. High frequency recovery via spectral match between surface seismic and crosswell seismic data. SEG Technical Program Expanded Abstracts, 2013, 32: 1 462–1 466.

    [13]Chen Jianjiang, Yin Xingyao. Three-parameter AVO waveform inversion based on Bayesian theorem. Chinese Journal of Geophysics, 2007, 50 (4): 1 251–1 260 (in Chinese).

This Article


CN: 13-1095/TE

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

December 2016


Article Outline


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