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Magnetotelluric inversion of one- and two-dimensional synthetic data based on hybrid genetic algorithms

  • Joelson da Conceição BatistaEmail author
  • Edson Emanoel Starteri Sampaio
Review Article - Applied Geophysics
  • 4 Downloads

Abstract

We applied the technique of the genetic algorithms and a local methodology integrating the Gauss–Newton and Conjugate Gradient (GNCG) techniques to test one-dimensional inverse modeling of synthetic magnetotelluric data. The result of this modeling applied to a homogeneous and isotropic five-layer model led to the development a hybrid algorithm (GAGNCG), combining the aforementioned techniques, for inverse modeling of one-dimensional magnetotelluric data. The GAGNCG modeling of the synthetic data performs more efficiently than the local methodology in terms of both procedure and results. This showed that the hybridization procedure maximized the advantages of using the global search methodology and minimized the disadvantages of the local technique. Based on these results, we developed another hybrid methodology (GA2D), built from some characteristics of the genetic algorithm and the simulated annealing method, for the inverse modeling of two-dimensional magnetotelluric data. The results were satisfactory, and the GA2D algorithm was a good starting point for the inverse modeling of two-dimensional data.

Keywords

Magnetotelluric Inversion Hybrid genetic algorithms 

Notes

Acknowledgements

We acknowledge our fellowships from CNPq (Brazilian National Council of Research). We also thank Dr. L. S. Batista for providing useful information about the FE method and the inversion codes.

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Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.Institute of Geosciences of the Federal University of BahiaSalvadorBrazil

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