Applied Geophysics

, Volume 6, Issue 2, pp 166–174 | Cite as

Swarm intelligence optimization and its application in geophysical data inversion

  • Sanyi YuanEmail author
  • Shangxu Wang
  • Nan Tian
Technical Papers Nonlinear Integrated Inversion


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.


Swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Chen, Y., 2004, Ant colony system for continuous function optimization: Journal of Sichuan University (Engineering Science Edition), 36(6), 117–120.Google Scholar
  2. 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.Google Scholar
  3. 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.Google Scholar
  4. 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
  5. Eberhart, R. C., and Shi, Y., 2004, Guest editorial special issue on particle swarm optimization: IEEE Transactions on Evolutionary Computation, 8(3), 201–203.CrossRefGoogle Scholar
  6. Holland, J. H., 1975, Adaptation in natural and artificial systems: The University of Michigan Press, Ann Arbor.Google Scholar
  7. 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.CrossRefGoogle Scholar
  8. Kennedy, J., and Eberhart, R. C., 1995, Particle swarm optimization: IEEE International Conference on Neural Networks, IV. Piscataway, NJ, IEEE Service Center, 1942–1948.Google Scholar
  9. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., 1983, Optimization by simulated annealing: Science, 220(4598), 671–680.CrossRefGoogle Scholar
  10. 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
  11. 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
  12. Ma, X. Q., 2002, Simultaneous inversion of prestack seismic data for rock properties using simulated annealing: Geophysics, 67(6), 1877–1885.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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
  15. Shaw, R., and Srivastava S., 2007, Particle swarm optimization: A new tool to invert geophysical data: Geophysics, 72(2), 75–83.CrossRefGoogle Scholar
  16. Sen, M. K., and Stoffa, P. L., 1991, Nonlinear one-dimensional seismic waveform inversion using simulated annealing: Geophysics, 56, 1624–1638.Google Scholar
  17. 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
  18. Stoffa, P. L., and Sen, M. K., 1991, Non-linear multiparameter optimization using genetic algorithm: Inversion of plane-wave seismograms: Geophysics, 56, 1794–1810.CrossRefGoogle Scholar
  19. Wang, J. Y., 2002, Inverse theory in geophysics: Higher Education Press (in Chinese), Beijing.Google Scholar
  20. Yang, W. C., 1997, Theory and methods of geophysical inversion: Geological Publishing House (in Chinese), Beijing.Google Scholar
  21. 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.Google Scholar
  22. 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
  23. 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
  24. 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
  25. 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.CrossRefGoogle Scholar

Copyright information

© Editorial Office of Applied Geophysics and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.CNPC Key Laboratory of Geophysical Exploration, Key Laboratory of Earth Prospecting and Information TechnologyChina University of PetroleumBeijingChina

Personalised recommendations