Mini-expert Platform for Pareto Multi-objective Optimization of Geophysical Problems

  • Adrian Bogacz
  • Tomasz Danek
  • Katarzyna MiernikEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


In this paper, a mini-expert platform for joint inversion is presented. The Pareto inversion scheme was applied to eliminate any typical problems of this kind of inversion, such as arbitrarily chosen target function weights and laborious interactivity. Particle Swarm Optimization was used as the main optimization engine. The presented solution is written entirely in JavaScript and provides easy access to core system functions, even for non-technical users. As an example, a geophysical problem of joint inversion of surface waves was chosen, but the solution is capable of inverting any kind of data as long as two or more target functions can be provided. All obtained results were compared with software written by the authors in C in terms of both results and efficiency.


Pareto inversion JavaScript Scientific computing PSO 



The paper has been prepared under the AGH-UST statutory research grant No.


  1. 1.
    Bijani, R., Lelivre, P.G., Ponte-Neto, C.F., Farquharson, C.G.: Physical-property-, lithology- and surface-geometry-based joint inversion using Pareto multi-objective global optimization. Geophys. J. Int. 209(2), 730–748 (2017). Scholar
  2. 2.
    Bogacz, A., Dalton, D.R., Danek, T., Miernik, K., Slawinski, M.A.: On Pareto Joint Inversion of guided waves. arXiv e-prints, December 2017Google Scholar
  3. 3.
    Dalton, D.R., Slawinski, M.A., Stachura, P., Stanoev, T.: Forward problem for Love and quasi-Rayleigh waves: exact dispersion relations and their sensitivities. arXiv e-prints, July 2016Google Scholar
  4. 4.
    Dalton, D.R., Slawinski, M.A., Stachura, P., Stanoev, T.: Sensitivity of love and quasi-rayleigh waves to model parameters. Q. J. Mech. Appl. Math. 70(2), 103–130 (2017). Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Kennedy, J.: Bare bones particle swarms. In: Proceeding of the IEEE Swarm Intelligence Symposium, pp. 80–87, April 2003Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, November 1995Google Scholar
  8. 8.
    Kozlovskaya, E., Vecsey, L., Plomerov, J., Raita, T.: Joint inversion of multiple data types with the use of multiobjective optimization: problem formulation and application to the seismic anisotropy investigations. Geophys. J. Int. 171(2), 761–779 (2007). Scholar
  9. 9.
    Miernik, K., Bogacz, A., Kozubal, A., Danek, T., Wojdyła, M.: Pareto joint inversion of 2D magnetotelluric and gravity data—towards practical applications. Acta Geophysica 64(5), 1655–1672 (2016). Scholar
  10. 10.
    Pan, F., Hu, X., Eberhart, R., Chen, Y.: An analysis of bare bones particle swarm. In: Proceeding of the IEEE Swarm Intelligence Symposium, pp. 21–23, September 2008Google Scholar
  11. 11.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization: an overview. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  12. 12.
    Sierra, M., Coello, C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006).
  13. 13.
    Vallina, A.: Principles of Seismology. Cambridge University Press, Cambridge (1999)Google Scholar
  14. 14.
    Vozoff, K., Jupp, D.L.B.: Joint inversion of geophysical data. Geophys. J. R. Astron. Soc. 42(3), 977–991 (1975). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Adrian Bogacz
    • 1
  • Tomasz Danek
    • 1
  • Katarzyna Miernik
    • 1
    Email author
  1. 1.AGH - University of Science and TechnologyKrakowPoland

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