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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2018: Bio-inspired Computing: Theories and Applications pp 60–69Cite as

Improved Clonal Selection Algorithm for Solving AVO Elastic Parameter Inversion Problem

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

Abstract

Amplitude Variation with Offset (AVO) elastic parameter inversion is a nonlinear optimization problem. When a linear or quasi-linear method is used to solve the problem, the inversion result will be unreliable or inaccurate. In this paper, the immune clonal selection algorithm is applied to the AVO elastic parameter inversion problem. The algorithm adopts the specific initialization strategy from Aki’s and Rechard’s approximation equation used in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is accordingly improved. A large number of experiments show that this method can significantly improve inversion accuracy.

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References

  1. Neidell, N.S.: Amplitude variation with offset. Lead. Edge 5(3), 47–51 (1986)

    Article  Google Scholar 

  2. Li, S.P.: AVO seismic parameter inversion method and its application. China University of Petroleum (2009, in Chinese)

    Google Scholar 

  3. Chen, J.J.: Inversion method of the three AVO parameters. China University of Petroleum (2007, in Chinese)

    Google Scholar 

  4. Wang, L.P.: Prestack AVO non-linear inversion of intelligent optimization algorithm. China University of Geosciences (2015, in Chinese)

    Google Scholar 

  5. Berg, E.: Simple convergent genetic algorithm for inversion of multiparameter data. In: SEG Technical Program Expanded Abstracts 1990, pp. 1126–1128. Society of Exploration Geophysicists (1990)

    Google Scholar 

  6. Porsani, M.J.: A combined genetic and linear inversion algorithm for seismic waveform inversion. In: SEG Technical Program Expanded Abstracts 1993, pp. 692–695. Society of Exploration Geophysicists (1993)

    Google Scholar 

  7. Mallick, S.: Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. J. Geophys. 60(4), 939–954 (1995)

    Article  Google Scholar 

  8. Priezzhev, I.I., Shmaryan, L.E., Bejarano, G.: 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 Discoveries (2008)

    Google Scholar 

  9. Soupios, P., Akca, I., Mpogiatzis, P.: Applications of hybrid genetic algorithms in seismic tomography. J. App. Geophys. 75(3), 479–489 (2011)

    Article  Google Scholar 

  10. Bai, J.Y.: Nonlinear hybrid optimization algorithm for seismic impedance inversion. In: Beijing 2014 International Geophysical Conference and Exposition, Beijing, China, 21–24 April 2014. Society of Exploration Geophysicists and Chinese Petroleum Society (2014)

    Google Scholar 

  11. Agarwal, A., Sain, K., Shalivahan, S.: Traveltime and constrained AVO inversion using FDR PSO. In: SEG Technical Program Expanded Abstracts 2016, pp. 577–581. Society of Exploration Geophysicists (2016)

    Google Scholar 

  12. Sun, S.Z.: PSO non-linear pre-stack inversion method and the application in reservoir prediction. In: SEG Technical Program Expanded Abstracts 2012, pp. 1–5. Society of Exploration Geophysicists (2012)

    Google Scholar 

  13. Sun, S.Z., Liu, L.: A numerical study on non-linear AVO inversion using chaotic quantum particle swarm optimization. J. Seismic Explor. 23(4), 379–392 (2014)

    Google Scholar 

  14. Zhou, Y., Nie, Z., Jia, Z.: An improved differential evolution algorithm for nonlinear inversion of earthquake dislocation. J. Geodesy Geodyn. 5(4), 49–56 (2014)

    Article  Google Scholar 

  15. Gao, Z., Pan, Z., Gao, J.: Multimutation differential evolution algorithm and its application to seismic inversion. IEEE Trans. Geosci. Remote Sens. 54(6), 3626–3636 (2016)

    Article  Google Scholar 

  16. Yin, X.Y., Kong, S.S., Zhang, F.C.: Prestack AVO inversion based on differential evolution algorithm. Oil Geophys. Prospect. 48(4), 591–596 (2013)

    Google Scholar 

  17. Wu, Q.H., Wang, L.P., Zhu, Z.X.: Research of pre-stack AVO elastic parameter inversion problem based on hybrid genetic algorithm. Cluster Comput. 20(4), 3173–3783 (2017)

    Article  Google Scholar 

  18. Wu, Q., Zhu, Z.X., Yan, X.S.: Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm. Cluster Comput. 20(4), 2881–2890 (2017)

    Article  Google Scholar 

  19. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  20. Gong, M., Jiao, L., Zhang, L.: Baldwinian learning in clonal selection algorithm for optimization. J. Inf. Sci. 180(8), 1218–1236 (2010)

    Article  Google Scholar 

  21. Feng, J., Jiao, L.C., Zhang, X.: Bag-of-visual-words based on clonal selection algorithm for SAR image classification. IEEE Geosci. Remote Sens. Lett. 8(4), 691–695 (2011)

    Article  Google Scholar 

  22. Karoum, B., Elbenani, Y.B.: A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings. J. Prod. Eng. 2017(15), 1–12 (2017)

    Google Scholar 

  23. Rao, B.S., Vaisakh, K.: Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty. Int. J. Bio-Inspir. Comput. 8(2), 67 (2016)

    Article  Google Scholar 

  24. Swain, R.K., Barisal, A.K., Hota, P.K.: Short-term hydrothermal scheduling using clonal selection algorithm. Int. J. Electr. Power Energy Syst. 33(3), 647–656 (2011)

    Article  Google Scholar 

  25. Chitsaz, H., Amjady, N., Zareipour, H.: Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm. J. Energy Convers. Manage. 89, 588–598 (2015)

    Article  Google Scholar 

  26. Sindhuja, L.S., Padmavathi, G.: Replica node detection using enhanced single hop detection with clonal selection algorithm in mobile wireless sensor networks. Hindawi Publishing Corp (2016)

    Google Scholar 

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Acknowledgments

This paper is supported by Natural Science Foundation of China. (No. 61673354, 41404076), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Zheng Li, Xuesong Yan & Yuanyuan Fan

  2. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Ke Tang

Authors
  1. Zheng Li
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  2. Xuesong Yan
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  3. Yuanyuan Fan
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  4. Ke Tang
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Corresponding author

Correspondence to Xuesong Yan .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Li, Z., Yan, X., Fan, Y., Tang, K. (2018). Improved Clonal Selection Algorithm for Solving AVO Elastic Parameter Inversion Problem. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_6

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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