DOI QR코드

DOI QR Code

Layered-earth Resistivity Inversion of Small-loop Electromagnetic Survey Data using Particle Swarm Optimization

입자 군집 최적화법을 이용한 소형루프 전자탐사 자료의 층서구조 전기비저항 역해석

  • Jang, Hangilro (Archaeology Study Division, National Research Institute of Cultural Heritage)
  • Received : 2019.10.08
  • Accepted : 2019.11.25
  • Published : 2019.11.30

Abstract

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.

물리탐사 자료의 역산 해를 찾는데 흔히 이용되는 결정론적 해법은 지역 최소점에 빠져 적절한 해에 수렴하지 못할 가능성이 크다는 단점이 존재한다. 이 문제를 해결하기 위한 대안 중 하나는 확률론적 접근법에 기반한 전역 최적화 방법을 이용하는 것이며, 여러 방법들 중에서 입자 군집 최적화(Particle Swarm Optimization, PSO)법의 적용사례가 많이 소개되었다. 이 논문에서는 PSO법을 이용한 소형루프 전자탐사 자료의 층서 구조 전기비저항 역해석 알고리즘을 개발하고 합성자료를 이용하여 역산실험을 수행하였다. 실험결과 기존의 Gauss-Newton 알고리즘으로는 최적의 역산해를 찾는데 어려움이 있는 소형루프 전자탐사 자료의 역산 시도에 PSO 방법을 적용하면 성공률을 높일 수 있음을 확인하였다.

Keywords

References

  1. Choi, H. W., Byun, J. M., and Seol, S. J., 2010, Automatic velocity analysis using bootstrapped differential semblance and global search methods, Geophys. and Geophys. Explor., 13(1), 31-39 (in Korean with English abstract). https://doi.org/10.1071/EG10004
  2. Cui, Y., Chen, Z., Zhu, X., Liu, H., and Liu, J., 2017, Sequential and simultaneous joint inversion of resistivity and IP sounding data using particle swarm optimization, J. Earth Sci., 28(4), 709-718. https://doi.org/10.1007/s12583-017-0749-1
  3. Essa, K. S., and Elhussein, M., 2018, PSO (particle swarm optimization) for interpretation of magnetic anomalies caused by simple geometrical structures, Pure. Appl. Geophys., 175(10), 3539-3553. https://doi.org/10.1007/s00024-018-1867-0
  4. Fernandez Martinez, J. L., Gonzalo, E. G., Fernandez Alvarez, J. P., Kuzma, H. A., and Menendez Perez, C. O., 2010, PSO: A powerful algorithm to solve geophysical inverse problems: Application to a 1D-DC resistivity case, J. Appl. Geophys., 71(1). 13-25.
  5. Geem, Z. W., Kim, J. H., and Loganathan, G. V., 2001, A new heuristic optimization algorithm: harmony search, Simulation, 76(2), 60-68. https://doi.org/10.1177/003754970107600201
  6. Glover, F., 1986, Future paths for integer programming and links to artificial intelligence, Comput. Oper. Res., 13(5), 533-549. https://doi.org/10.1016/0305-0548(86)90048-1
  7. Goldberg, D. E., 1989, Genetic algorithms in search, optimization and machine Learning, Kluwer Academic Publishers.
  8. Jang, H., and Kim, H. J., 2015, Mapping deep-sea hydrothermal deposits with an in-loop transient electromagnetic method: Insights from 1D forward and inverse modeling, J. Appl. Geophys., 123, 170-176. https://doi.org/10.1016/j.jappgeo.2015.10.003
  9. Kennedy, J., and Eberhart, R., 1995, Particle swarm Optimization, Proc. IEEE Int. Conf. Neural Netw., 1942-1948.
  10. Kennedy, J., and Eberhart, R. C., 2001, Swarm intelligence, Morgan Kaufmann.
  11. Kim, H. J., 1995a, Inversion of geophysical data via simulated Annealing, Econ. Environ. Geol., 28(3), 305-309 (in Korean with English abstract).
  12. Kim, H. J., 1995b, Inversion of geophysical data using genetic algorithms, Econ. Environ. Geol., 28(4), 425-431 (in Korean with English abstract).
  13. Kim, H. J., Choi, J. H., Han, N. R., Song, Y. H., and Lee, K. H., 2009, Primary solution evaluations for interpreting electromagnetic data, Geophys. and Geophys. Explor., 12(4), 361-366 (in Korean with English abstract).
  14. Kirkpatrick, S., and Gelatt Jr., C. D., and Vecchi, M. P., 1983, Optimization by simulated annealing, Science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671
  15. Koh, Y. M., and Ree, S. W., 2005, Paul Erdos and probabilistic methods, J. Hist. Math., 18(4), 101-112 (in Korean with English abstract).
  16. Li, M., Cheng, J., Wang, P., Xiao, Y., Yao, W., Su, C., Cheng, S., Guo, J., and Yu, X., 2019, Transient electromagnetic 1D inversion based on the PSO-DLS combination algorithm, Explor. Geophys., 50(5), 1-9. https://doi.org/10.1080/08123985.2018.1548605
  17. Oh, S. H., Kwon, B. D., and Suh, B. S., 1997, Nonlinear inversion of resistivity sounding data using simulated annealing, J. Korean Soc. Miner. Energy Resour. Eng., 34, 285-293 (in Korean with English abstract).
  18. Pace, F., Godio, A., and Santilano, A., 2019, Joint optimization of geophysical data using multi-objective swarm intelligence, Geophys. J. Int., 218(3), 1502-1521. https://doi.org/10.1093/gji/ggz243
  19. Park, H., and Hwang, H., 2012, Development of automated inversion method for HWAW method using genetic algorithm, J. Korean Geotech. Soc., 28(8), 55-63 (in Korean with English abstract). https://doi.org/10.7843/kgs.2012.28.8.55
  20. Park, I., and Kim, K. Y., 2015, Receiver function inversion beneath Ngauruhoe volcano, New Zealand, using the genetic algorithm, Geophys. and Geophys. Explor., 18(1), 1-8 (in Korean with English abstract). https://doi.org/10.7582/GGE.2015.18.1.001
  21. Santilano, A., Godio, A., and Manzella, A., 2018, Particle swarm optimization for simultaneous analysis of magnetotelluric and time-domain electromagnetic data, Geophysics, 83(3), E151-E159. https://doi.org/10.1190/geo2017-0261.1
  22. Santilano, A., Godio, A., and Pace, F., 2019, Particle swarm optimization of 2D magnetotelluric data, Geophysics, 84(3), 1-59. https://doi.org/10.1190/geo2019-0620-tiogeo.1
  23. Shah-Hosseini, H., 2009, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, Int. J. Bio-inspir. Com., 1(1/2), 71-79. https://doi.org/10.1504/IJBIC.2009.022775
  24. Shaw, R., and Srivastava, S., 2007, Particle swarm optimization: A new tool to invert geophysical data, Geophysics, 72(2), F75-F83. https://doi.org/10.1190/1.2432481
  25. Singh, K., K., and Singh, U. K., 2017, Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast, Geosci. Instrum. Meth., 6, 193-198. https://doi.org/10.5194/gi-6-193-2017
  26. Ward, S. H., and Hohmann, G. W., 1987, Electromagnetic Theory for Geophysical Applications, in Nabighian, M. N., Ed., Electromagnetic Methods in Applied Geophysics, Vol. 1, Soc. Expl. Geophys., 131-311.
  27. Wilken, D., and Rabbel, W., 2012, On the application of Particle Swarm Optimization strategies on Scholte-wave inversion, Geophys. J. Int., 190(1), 580-594. https://doi.org/10.1111/j.1365-246X.2012.05500.x
  28. Yang X. S., and Deb, S., 2010, Engineering optimisation by cuckoo search, Int. J. Math. Model. Numer. Optim., 1(4), 330-343. https://doi.org/10.1504/IJMMNO.2010.035430