Swarm intelligence optimization and its application in geophysical data inversion
Yuan San-Yi1, Wang Shang-Xu1, and Tian Nan1
1. CNPC Key Laboratory of Geophysical Exploration, Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum, Beijing 102249, China.
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.
YUAN San-Yi,WANG Shang-Xu,TIAN Nan. Swarm intelligence optimization and its application in geophysical data inversion[J]. APPLIED GEOPHYSICS, 2009, 6(2): 166-174.
[1]
Chen, Y., 2004, Ant colony system for continuous function optimization: Journal of Sichuan University (Engineering Science Edition), 36(6), 117 - 120.
[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.
[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.
[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.
[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.
[6]
Holland, J. H., 1975, Adaptation in natural and artificial systems: The University of Michigan Press, Ann Arbor.
[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.
[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.
[9]
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., 1983, Optimization by simulated annealing: Science, 220(4598), 671 - 680.
[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.
[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.
[12]
Ma, X. Q., 2002, Simultaneous inversion of prestack seismic data for rock properties using simulated annealing: Geophysics, 67(6), 1877 - 1885.
[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.
[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.
[15]
Shaw, R., and Srivastava S., 2007, Particle swarm optimization: A new tool to invert geophysical data: Geophysics, 72(2), 75 - 83.
[16]
Sen, M. K., and Stoffa, P. L., 1991, Nonlinear one-dimensional seismic waveform inversion using simulated annealing: Geophysics, 56, 1624 - 1638.
[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.
[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.
[19]
Wang, J. Y., 2002, Inverse theory in geophysics: Higher Education Press (in Chinese), Beijing.
[20]
Yang, W. C., 1997, Theory and methods of geophysical inversion: Geological Publishing House (in Chinese), Beijing.
[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.
[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.
[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.
[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.
[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.
2009, Vol. 6 
