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

• Autorzy: da Conceição Batista Joelson , Starteri Sampaio Edson Emanoel

Identyfikatory

DOI

10.1007/s11600-019-00325-y

• Języki publikacji: EN

Abstrakty

EN

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 efciently 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.

Słowa kluczowe

EN

magnetotelluric; inversion; hybrid genetic algorithm

PL

magnetotelluryka; odwrócenie; hybrydowy algorytm genetyczny

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 5
• Strony: 1365--1377
• Opis fizyczny: Bibliogr. 31 poz.

Twórcy

autor

da Conceição Batista Joelson

• Institute of Geosciences of the Federal University of Bahia, # 265-E, University Campus of Ondina, Salvador, Bahia, Brazil

autor

Starteri Sampaio Edson Emanoel

• Institute of Geosciences of the Federal University of Bahia, # 265-E, University Campus of Ondina, Salvador, Bahia, Brazil

Bibliografia

• 1. Batista JC (2013) Modeling and Interpretation of Magnetotelluric Data in the Tucano Basin. Doctoral Thesis, Federal University of Bahia, Salvador (in Portuguese)
• 2. Batista LS, Porsani MJ (1991) Technical optimization of finite element modeling for electromagnetic geophysics. In: 2nd Brazilian Geophysical Congress, vol 1, Salvador, Bahia, Brazil
• 3. Becker BE, Garey GF, Oden JT (1981) Finite elements. Prentice-Hall Inc, Englewood, p 258
• 4. Cagniard L (1953) Basic theory of the magneto-telluric method of geophysical prospecting. Geophysics 18:605–635. https://doi.org/10.1190/1.1437915
• 5. Chunduru R, Sen MK, Stoffa PL, Nagendra R (1995) Non-linear inversion of resistivity profiling data for some geometrical bodies. Geophys Prospect 43:979–1003. https://doi.org/10.1111/j.1365-2478.1995.tb00292.x
• 6. Corana A, Marchesi M, Martini C, Ridella S (1987) Minimizing multimodal functions of continuous variables with the simulated annealing algorithm. ACM Trans Math Softw 13:262–280. https://doi.org/10.1145/29380.29864
• 7. de Groot-Hedlin C, Constable S (1990) Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics 55:1613–1624. https://doi.org/10.1190/1.1442813
• 8. Ferreira NR, Porsani MJ, Oliveira SP (2003) A hybrid genetic-linear algorithm for 2D inversion of sets of vertical electrical sounding. Braz J Geophys 21:235–248. https://doi.org/10.1590/S0102-261X2003000300003
• 9. Gersztenkorn A, Bednard JB, Lines LR (1986) Robust iterative inversion for the one-dimensional acoustic wave equation. Geophysics 51:357–368
• 10. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York, p 403
• 11. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, p 14
• 12. Kelbert A, Meqbel N, Egbert GD, Tandon K (2014) ModEM: a modular system for inversion of electromagnetic geophysical data. Comput Geosci 66:40–53. https://doi.org/10.1016/j.cageo.2014.01.010
• 13. Mcgillivray P, Oldenburg DW (1990) Methods for calculating Fréchet derivatives and sensitivities for non-linear inverse problem: a comparative study. Geophys Prospect 38:499–524
• 14. Menke W (1989) Geophysical data analysis: discrete inverse theory. Academic Press, New York
• 15. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1092. https://doi.org/10.1063/1.1699114
• 16. Porsani MJ, Stoffa PL, Sen MK, Chunduru R, Wood WT (1993) A combined genetic and linear inversion algorithm for seismic waveform inversion. In: SEG technical program expanded abstracts, Society of Exploration Geophysicists, pp 692–695, https://doi.org/10.1016/S0921-9366(06)80009-5
• 17. Porsani MJ, Stoffa PL, Sen MK, Chunduru RK (2000) Fitness functions, genetic algorithms and hybrid optimization in seismic waveform inversion. J Seism Explor 9:143–164
• 18. Ramos RGN, Sampaio EES (1993) A comparative study of the asymptotic techniques mostly employed in the interpretation of magnetotelluric soundings. Braz J Geophys 11:65–79
• 19. Rodi W, Mackie RL (2001) Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion. Geophysics 66:174–187. https://doi.org/10.1190/1.1444893
• 20. Rothman DH (1985) Nonlinear inversion, statistical mechanics and residual static estimation. Geophysics 50:2784–2796. https://doi.org/10.1190/1.1441899
• 21. Santos AB, Sampaio EES, Porsani MJ (2005) A robust two-step inversion of complex magnetotelluric apparent resistivity data. Studia Geophysica et Geodaetica 49(1):109–125
• 22. Sen MK, Stoffa PL (1995) Global optimization methods in geophysical inversion. Elsevier, Amsterdam, p 281
• 23. Sen MK, Bhattacharya BB, Stoffa PL (1993) Nonlinear inversion of resistivity sounding data. Geophysics 58(4):496–507. https://doi.org/10.1190/1.1443432
• 24. Siripunvaraporn W, Egbert G (2000) An efficient data-subspace inversion method for 2-D magnetotelluric data. Geophysics 65:791–803. https://doi.org/10.1190/1.1444778
• 25. Siripunvaraporn W, Egbert G (2009) WSINV3DMT: vertical magnetic field transfer function inversion and parallel implementation. Phys Earth Planet Inter 173:317–329. https://doi.org/10.1016/j.pepi.2009.01.013
• 26. Stratton JA (1941) Electromagnetic theory. MaGraw-Hill, New York, p 615
• 27. Tarantola A, Valette B (1982) Inverse problems = quest for information. J Geophys 50:159–170
• 28. Tikhonov A (1950) Determination of the electrical characteristics of the deep strata of the Earth’s crust. Doklady 73:295–297
• 29. Travis BJ, Chave AD (1989) A moving finite element method for magnetotelluric modeling. Phys Earth Planet Inter 53:432–443. https://doi.org/10.1016/0031-9201(89)90028-9
• 30. Ward SH, Hohmann GW (1988) Electromagnetic theory for geophysical applications. Electromagn Methods Appl Geophys 1:130–311
• 31. Whittall KP, Oldenburg DW (1992) Inversion of magnetotelluric data for a one-dimensional conductivity. In: Fitterman DV (ed) Geophysics monograph series no. 5. Society of Exploration Geophysicists, Tulsa, Oklahoma

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).