Elastic parameter inversion problem based on brain storm optimization algorithm

  • Xuesong Yan
  • Zhixin Zhu
  • Qinghua WuEmail author
  • Wenyin Gong
  • Ling Wang
Regular Research Paper


The pre-stack Amplitude Variation with Offset (AVO) elastic parameter inversion technique combined with an intelligent optimization algorithm provides a more effective identification method for oil and gas exploration. However, biological evolution-based optimization algorithms, such as genetic algorithm, generally suffer problems such as premature convergence and high probability of becoming trapped in a local optimum, and these problems lead to unsatisfactory inversion results. To solve the above problems, this paper proposes a swarm-intelligence-based brain storm optimization algorithm, which is more suitable for solving the inversion problem of pre-stack AVO elastic parameters. The algorithm employs a specific initialization strategy for Aki and Rechard’s approximation equation, which is used in the inversion process, to produce a smoother initialization parameter curve. Multiple experiments prove that the correlation coefficients of the elastic parameters obtained by inversion are high, while the inversion accuracy is improved significantly.


Brain storm optimization algorithm Pre-stack AVO Elastic parameter inversion Correlation coefficient 



This paper is supported by Natural Science Foundation of China (No. 61673354, 61573324 and 41404076 ), National Natural Science Foundation for Distinguished Young Scholars of China (No. 61525304), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology(DMETKF2018020) and the State Key Laboratory of Intelligent Control and Decision of Complex Systems.


  1. 1.
    Agarwal A, Sain K, Shalivahan S (2016) Traveltime and constrained avo inversion using fdr pso. In: SEG technical program expanded abstracts 2016, Society of Exploration Geophysicists, pp 577–581Google Scholar
  2. 2.
    Berg E, et al (1990) Simple convergent genetic algorithm for inversion of multiparameter data. In: 1990 SEG annual meeting, Society of Exploration GeophysicistsGoogle Scholar
  3. 3.
    Cao Z, Shi Y, Rong X, Liu B, Du Z, Yang B (2015) Random grouping brain storm optimization algorithm with a new dynamically changing step size. In: International conference in swarm intelligence, Springer, pp 357–364Google Scholar
  4. 4.
    Chen J, Wang J, Cheng S, Shi Y (2016) Brain storm optimization with agglomerative hierarchical clustering analysis. In: International conference in swarm intelligence, Springer, pp 115–122Google Scholar
  5. 5.
    Cheng S, Shi Y, Qin Q, Zhang Q, Bai R (2014) Population diversity maintenance in brain storm optimization algorithm. J Artifif Intell Soft Comput Res 4(2):83–97Google Scholar
  6. 6.
    Deng J, Wang L (2017) A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol Comput 32:121–131CrossRefGoogle Scholar
  7. 7.
    El-Abd M (2017) Global-best brain storm optimization algorithm. Swarm Evol Comput 37:27–44CrossRefGoogle Scholar
  8. 8.
    Gong W, Yan X, Liu X, Cai Z (2015) Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy 86:139–151CrossRefGoogle Scholar
  9. 9.
    Junyu B, Zilong X, Yunfei X, Tianshou X (2014) Nonlinear hybrid optimization algorithm for seismic impedance inversion. In: Beijing 2014 international geophysical conference & exposition, Beijing, China, 21-24 April 2014, Society of Exploration Geophysicists and Chinese Petroleum Society, pp 541–544Google Scholar
  10. 10.
    Mallick S (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4):939–954CrossRefGoogle Scholar
  11. 11.
    Neidell NS (1986) Amplitude variation with offset. Leadi Edge 5(3):47–51CrossRefGoogle Scholar
  12. 12.
    Porsani MJ, Stoffa PL, Sen MK, Chunduru R, Wood WT (1993) A combined genetic and linear inversion algorithm for seismic waveform inversion. In: SEG technical program expanded abstracts 1993, Society of Exploration Geophysicists, pp 692–695Google Scholar
  13. 13.
    Priezzhev I, Shmaryan L, Bejarano G (2008) Nonlinear multitrace seismic inversion using neural network and genetic algorithm. In: 3rd EAGE St. Petersburg international conference and exhibition on geosciences-geosciences: from new ideas to new discoveriesGoogle Scholar
  14. 14.
    Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm intelligence, Springer, pp 303–309Google Scholar
  15. 15.
    Soupios P, Akca I, Mpogiatzis P, Basokur AT, Papazachos C (2011) Applications of hybrid genetic algorithms in seismic tomography. J Appl Geophy 75(3):479–489CrossRefGoogle Scholar
  16. 16.
    Sun SZ, Liu L (2014) A numerical study on non-linear avo inversion using chaotic quantum particle swarm optimization. J Seism Explor 23(4):379–392Google Scholar
  17. 17.
    Sun SZ, Chen L, Bai Y, Hu L (2012) Pso non-linear pre-stack inversion method and the application in reservoir prediction. In: SEG technical program expanded abstracts 2012, Society of Exploration Geophysicists, pp 1–5Google Scholar
  18. 18.
    Tang K, Yang P, Yao X (2016) Negatively correlated search. IEEE J Sel Areas Commun 34(3):542–550CrossRefGoogle Scholar
  19. 19.
    Wang L (2015) Pre-stack avo nonlinear inversion with intelligent optimization algorithm. Master’s thesis, China University of GeosciencesGoogle Scholar
  20. 20.
    Wu Q, Liu H, Yan X (2016) Multi-label classification algorithm research based on swarm intelligence. Clust Comput 19(4):2075–2085CrossRefGoogle Scholar
  21. 21.
    Wu Q, Wang L, Zhu Z (2017a) Research of pre-stack avo elastic parameter inversion problem based on hybrid genetic algorithm. Clust Comput 20(4):3173–3183CrossRefGoogle Scholar
  22. 22.
    Wu Q, Zhu Z, Yan X (2017b) Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm. Clust Comput 20(2):2881–2890CrossRefGoogle Scholar
  23. 23.
    Xuesong Y, Jie S, Chengyu H (2017) Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Clust Comput 20(2):1007–1016CrossRefGoogle Scholar
  24. 24.
    Yan X, Liu H, Zhu Z, Wu Q (2017a) Hybrid genetic algorithm for engineering design problems. Clust Comput 20(1):263–275CrossRefGoogle Scholar
  25. 25.
    Yan X, Song T, Wu Q (2017b) An improved cultural algorithm and its application in image matching. Multimed Tools Appl 76(13):14,951–14,968CrossRefGoogle Scholar
  26. 26.
    Yan X, Zhao J, Hu C, Zeng D (2017c) Multimodal optimization problem in contamination source determination of water supply networks. Swarm Evol Comput.
  27. 27.
    Yan X, Li T, Hu C, Wu Q (2018a) Real-time localization of pollution source for urban water supply network in emergencies. Clust Comput.
  28. 28.
    Yan X, Zhu Z, Wu Q (2018b) Intelligent inversion method for pre-stack seismic big data based on mapreduce. Comput Geosci 110:81–89CrossRefGoogle Scholar
  29. 29.
    Zhan Zh, Zhang J, Shi Yh, Liu Hl (2012) A modified brain storm optimization. In: IEEE congress on evolutionary computation (CEC), 2012, IEEE, pp 1–8Google Scholar
  30. 30.
    Zhou D, Shi Y, Cheng S (2012) Brain storm optimization algorithm with modified step-size and individual generation. In: Advances in swarm intelligence pp 243–252Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xuesong Yan
    • 1
    • 2
  • Zhixin Zhu
    • 1
  • Qinghua Wu
    • 3
    Email author
  • Wenyin Gong
    • 1
  • Ling Wang
    • 4
  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.Faculty of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina
  4. 4.Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations