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Swarm intelligence optimization and its application in geophysical data inversion

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  • Nonlinear Integrated Inversion
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Abstract

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems: (1) a linear problem which is sensitive to noise, (2) a synchronous inversion of linear and nonlinear problems, and (3) a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

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References

  • Chen, Y., 2004, Ant colony system for continuous function optimization: Journal of Sichuan University (Engineering Science Edition), 36(6), 117–120.

    Google Scholar 

  • Chen, S. Q., Wang, S. X., and Zhang, Y. G., 2005, Ant colony optimization for the seismic nonlinear inversion: 75th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1732–1734.

  • Colorni, A., Dorigo, M., and Maniezzo, V., 1991, Distributed optimization by ant colonies: Proceedings of the First European Conference on Artificial Life, Paris, 134–142.

  • Dorigo, M., Maniezzo, V., and Colorni, A., 1996, The ant system: optimization by a colony of cooperating agents: IEEE Transactions on Systems, Man, and Cybernetics-Part B (S1083-4419), 26(1), 29–41.

    Google Scholar 

  • Eberhart, R. C., and Shi, Y., 2004, Guest editorial special issue on particle swarm optimization: IEEE Transactions on Evolutionary Computation, 8(3), 201–203.

    Article  Google Scholar 

  • Holland, J. H., 1975, Adaptation in natural and artificial systems: The University of Michigan Press, Ann Arbor.

    Google Scholar 

  • Hu, B., Tang, G., Ma, J. W., and Yang, H. Z., 2007, Parametric inversion of viscoelastic media from VSP data using a genetic algorithm: Applied Geophysics, 4(3), 194–200.

    Article  Google Scholar 

  • Kennedy, J., and Eberhart, R. C., 1995, Particle swarm optimization: IEEE International Conference on Neural Networks, IV. Piscataway, NJ, IEEE Service Center, 1942–1948.

  • Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., 1983, Optimization by simulated annealing: Science, 220(4598), 671–680.

    Article  Google Scholar 

  • Li, S. Y., and Li, P. C., 2007, Quantum particle swarms algorithm for continuous space optimization: Chinese Journal of Quantum Electronics (in Chinese), 24(5), 569–574.

    Google Scholar 

  • Luo, H. M., Wang, J. Y., Zhu, P. M., Shi, X. M., and He, G. M., 2008, Study of geophysical inversion based on immunity algorithm: Oil Geophysical Prospecting (in Chinese), 43(2), 222–228.

    Google Scholar 

  • Ma, X. Q., 2002, Simultaneous inversion of prestack seismic data for rock properties using simulated annealing: Geophysics, 67(6), 1877–1885.

    Article  Google Scholar 

  • Pan, B. Z., Xue, L. F., Huang, B. Z., Yan, G. J., and Zhang, L. H., 2008, Evaluation of volcanic reservoirs with the “QAPM mineral model” using a genetic algorithm: Applied Geophysics, 5(1), 1–8.

    Article  Google Scholar 

  • Peng, X. Y., Peng, Y., and Dai, Y. F., 2003, Swarm intelligence theory and applications: Acta Electronica Sinica (in Chinese), 31(12A), 1982–1988.

    Google Scholar 

  • Shaw, R., and Srivastava S., 2007, Particle swarm optimization: A new tool to invert geophysical data: Geophysics, 72(2), 75–83.

    Article  Google Scholar 

  • Sen, M. K., and Stoffa, P. L., 1991, Nonlinear one-dimensional seismic waveform inversion using simulated annealing: Geophysics, 56, 1624–1638.

  • Shi, X. M., Wang, J. Y., Yi, Y. Y., Yuan, X. X., Wang, X. M., and Zhang, Y. M., 2007, A study on the simulated atomic transition algorithm for geophysical inversion: Chinese Journal of Geophysics (in Chinese), 50(1), 305–312.

    Google Scholar 

  • Stoffa, P. L., and Sen, M. K., 1991, Non-linear multiparameter optimization using genetic algorithm: Inversion of plane-wave seismograms: Geophysics, 56, 1794–1810.

    Article  Google Scholar 

  • Wang, J. Y., 2002, Inverse theory in geophysics: Higher Education Press (in Chinese), Beijing.

    Google Scholar 

  • Yang, W. C., 1997, Theory and methods of geophysical inversion: Geological Publishing House (in Chinese), Beijing.

    Google Scholar 

  • Yi, Y. Y., Yuan, S. Y., and Shi, X. M., 2007, Wave impedance inversion using PSO algorithm: Second International Symposium on Intelligence Computation and Applications, China, 712–714.

  • Yu, P., Dai, M. G., Wang, J. L., and Wu, J. S., 2008, Joint inversion of gravity and seismic data based on common grid model with random density and velocity distributions: Chinese Journal of Geophysics (in Chinese), 51(3), 845–852.

    Google Scholar 

  • Yu, Y., Li, Y. S., and Yu, X. C., 2008, Application of particle swarm optimization in the engineering optimization design: Chinese Journal of Mechanical Engineering (in Chinese), 44(12), 226–231.

    Google Scholar 

  • Zhang, H. B., Shang, Z. P., Yang, C. C., and Duan, Q. L., 2005, Estimation of regular parameters for the impedance inversion: Chinese Journal of Geophysics (in Chinese), 48(1), 181–188.

    Google Scholar 

  • Zhou, H., Takenaka, T., and Tanaka, T., 2005, Time-domain reconstruction of lossy objects using dipole antennas: Microwave and Optical Technology Letters, 44(3), 238–243.

    Article  Google Scholar 

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Authors and Affiliations

  1. CNPC Key Laboratory of Geophysical Exploration, Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum, Beijing, 102249, China

    Sanyi Yuan, Shangxu Wang & Nan Tian

Authors
  1. Sanyi Yuan
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  2. Shangxu Wang
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  3. Nan Tian
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Corresponding author

Correspondence to Sanyi Yuan.

Additional information

This research was financially supported by the 973 Program (Grant No 2007CB209600) and Open Fund (No. GDL0706) of the Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education.

Yuan Sanyi received a Bachelor’s degree of Prospecting Technology and Engineering at Yangtze University in 2006. He is now a PhD student at China University of Petroleum (Beijing). His research interests mainly includes spectral inversion, swarm intelligence inversion, and seismic data processing.

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Yuan, S., Wang, S. & Tian, N. Swarm intelligence optimization and its application in geophysical data inversion. Appl. Geophys. 6, 166–174 (2009). https://doi.org/10.1007/s11770-009-0018-x

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  • DOI: https://doi.org/10.1007/s11770-009-0018-x

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