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A HYBRID ALGORITHM FOR THE INVERSION OF DIRECT CURRENT RESISTIVITY AND MAGNETOTELLURIC DATA

Year 2021, Volume: 9 Issue: 1, 77 - 87, 30.03.2021
https://doi.org/10.21923/jesd.526705

Abstract

In this study, a hybrid algorithm was developed for the inversion of geophysical data. In the hybrid algorithm, singular value decomposition and very fast simulated annealing methods were sequentially used to obtain the best result. Very fast simulated annealing method is a global optimization method is used for pre-estimation of initial values of singular value decomposition. To test the inversion algorithm, developed one-dimensional direct current resistivity and magnetotelluric forward modeling codes were used. By using these codes, synthetic data were generated. Firstly, the inversion of data was calculated by using standard singular value decomposition and very fast simulated annealing methods. Subsequently, the inversion was repeated by using the developed hybrid algorithm. Parameters that were estimated and the run times of the codes were compared with each other. In addition, the results of field data were compared with the previous works. It was shown that in terms of run time and parameter estimation, the hybrid algorithm is more efficient than only the use of very fast simulated annealing and singular value decomposition methods.

References

  • Aster RC, Borchers B, Thurber CH, 2005. Parameter Estimation and Inverse Problems. London, UK: Elsevier.
  • Balkaya Ç., 2013. An implementation of differential evolution algorithm for inversion of geoelectrical data, Journal of Applied Geophysics, 98, 160-175.
  • Başokur, A.T., Akca, İ., Siyam, N., 2007. Hybrid genetic algorithms in view of the evolution theories with application for the electrical sounding method, Geophysical Prospecting, 55 (3), 393-406.
  • Başokur, A.T., 2015. Türev tabanlı parametre kestirim yöntemleri, TMMOB Jeofizik Mühendisleri Odası yayını, Ankara.
  • Cagniard, L., 1953. Basic Theory of the Magneto-telluric Method of Geophysical Prospecting, Geophysics, 18, 605-635.
  • Di Maio R., Rani P., Piegari E., Milano L., 2016. Self-potential data inversion trough a Genetic-Price algorithm, Computer & Geosciences, 94, 86-95.
  • Golub G.H., Reinsch, C., 1971. Singular value decomposition and least squares solutions, Numerical Mathematics, 13, 403-420.
  • Golub G.H., Van Loan C.F., 1996. Matrix Computations, Baltimore, MD, USA, Johns Hopkins University Press.
  • Göktürkler, G., 2018. A hybrid approach for tomographic inversion of crosshole seismic first-arrival times. Journal of Geophysics and Engineering 8 (1), 99-108.
  • Grant, F.S., West, G.F., 1965. Interpretation theory in applied geophysics. McGraw-Hill, New York.
  • Ingber, L., 1989. Very fast simulated reannealing. Mathematical and Computer Modeling, 12 (8), 967 –993.
  • Kara, K.B., Pekşen, E., 2017. 1D fullwaveform optimization using Gpr data, 9th Congress of the Balkan Geophysical Society, Antalya. doi:10.3997/2214-4609.201702521.
  • Koefoed, O., 1979. Geosounding principles resistivity sounding measurements. Elsevier, Amsterdam.
  • LaBrecque D.J, Heath G, Sharpe R, Versteeg R, 2004. Autonomous monitoring of fluid movement using 3-D electrical resistivity tomography, J Environ Eng Geoph. 9, 167-176.
  • Meju, M.A., 1994. Geophysical Data Analysis: Understanding Inverse Problem Theory and Practice, SEG, Tulsa.
  • Meju, M.A., 1992. An effective ridge regression procedure for resistivity data inversion, Computer & Geosciences, 18, 99-118.
  • Nguyen, L.T., Nestorovic, T., 2016. Unscented hybrid simulated annealing for fast inversion of tunnel seismic waves, Comput. Methods Apll. Mech. Engrg., 301, 281-299.
  • Sen, M., Stoffa, P., 2013. Global optimization methods in geophysical inversion. Elsevier, Amsterdam.
  • Sharma, S.P., 2011. VFSARES- a very fast simulated annealing Fortran program for interpretation of 1-D DC resistivity sounding data from various electrode arrays, Computer and Geosciences, 42 (C), 177–188.
  • Slaoui, F.H., Georges, S., Lagace P.J., Do, X.D., 2003. The inverse problem of Schlumberger resistivity sounding measurement by ridge regression, Electric Power Systems Research, 67, 109-114. Telford, W.M., Geldart, L.P. and Sheriff, R.E., 1990. Applied geophysics, Cambridge University Press, Cambridge.
  • Tikhonov, A.N, Arsenin, VY 1977. Solutions of Ill-Posed Problems. New York, NY, USA: Halsted Press.
  • Zhdanov, M.S., Keller, G. V., 1994. The Geophysical Methods in Geophysical Exploration, Elsevier.

DOĞRU AKIM ÖZDİRENÇ VE MANYETOTELLÜRİK VERİLERİNİN TERS ÇÖZÜMÜ İÇİN MELEZ ALGORİTMA

Year 2021, Volume: 9 Issue: 1, 77 - 87, 30.03.2021
https://doi.org/10.21923/jesd.526705

Abstract

Bu çalışmada jeofizik verilerin ters çözümü için melez bir algoritma geliştirilmiştir. Melez algoritmada tekil değer ayrışımı ve çok hızlı tavlama benzetimi yöntemleri ardışık olarak kullanılmıştır. Global optimizasyon yöntemi olan çok hızlı tavlama benzetimi yöntemi tekil değer ayırışımı yönteminin başlangıç parametrelerinin ön kestirimi için kullanılmıştır. Ters çözüm algoritmasını test etmek amacıyla bir boyutlu doğru akım özdirenç ve manyetotellürik düz çözüm programı geliştirilmiştir. Bu programlar kullanılarak verilerin ters çözümü yapılmıştır. İlk önce verilerin ters çözümü tek başına tekil değer ayrışımı ve çok hızlı tavlama benzetimi yöntemi kullanılarak yapılmıştır. Daha sonra ters çözüm, geliştirilen melez algoritma kullanılarak tekrarlanmıştır. Kestirilen parametreler ve programların çalışma süreleri birbirleri ile karşılaştırılmıştır. Ayrıca, arazi verilerinin sonuçları daha önce kestirilen parametreler ile karşılaştırılmıştır. Programların çalışma süresi ve parametre kestirimi açısından melez algoritmanın çok hızlı tavlama benzetimi ve tekil değer ayrımı yöntemlerinin tek başına kullanılmasından daha verimli olduğu gösterilmiştir.

References

  • Aster RC, Borchers B, Thurber CH, 2005. Parameter Estimation and Inverse Problems. London, UK: Elsevier.
  • Balkaya Ç., 2013. An implementation of differential evolution algorithm for inversion of geoelectrical data, Journal of Applied Geophysics, 98, 160-175.
  • Başokur, A.T., Akca, İ., Siyam, N., 2007. Hybrid genetic algorithms in view of the evolution theories with application for the electrical sounding method, Geophysical Prospecting, 55 (3), 393-406.
  • Başokur, A.T., 2015. Türev tabanlı parametre kestirim yöntemleri, TMMOB Jeofizik Mühendisleri Odası yayını, Ankara.
  • Cagniard, L., 1953. Basic Theory of the Magneto-telluric Method of Geophysical Prospecting, Geophysics, 18, 605-635.
  • Di Maio R., Rani P., Piegari E., Milano L., 2016. Self-potential data inversion trough a Genetic-Price algorithm, Computer & Geosciences, 94, 86-95.
  • Golub G.H., Reinsch, C., 1971. Singular value decomposition and least squares solutions, Numerical Mathematics, 13, 403-420.
  • Golub G.H., Van Loan C.F., 1996. Matrix Computations, Baltimore, MD, USA, Johns Hopkins University Press.
  • Göktürkler, G., 2018. A hybrid approach for tomographic inversion of crosshole seismic first-arrival times. Journal of Geophysics and Engineering 8 (1), 99-108.
  • Grant, F.S., West, G.F., 1965. Interpretation theory in applied geophysics. McGraw-Hill, New York.
  • Ingber, L., 1989. Very fast simulated reannealing. Mathematical and Computer Modeling, 12 (8), 967 –993.
  • Kara, K.B., Pekşen, E., 2017. 1D fullwaveform optimization using Gpr data, 9th Congress of the Balkan Geophysical Society, Antalya. doi:10.3997/2214-4609.201702521.
  • Koefoed, O., 1979. Geosounding principles resistivity sounding measurements. Elsevier, Amsterdam.
  • LaBrecque D.J, Heath G, Sharpe R, Versteeg R, 2004. Autonomous monitoring of fluid movement using 3-D electrical resistivity tomography, J Environ Eng Geoph. 9, 167-176.
  • Meju, M.A., 1994. Geophysical Data Analysis: Understanding Inverse Problem Theory and Practice, SEG, Tulsa.
  • Meju, M.A., 1992. An effective ridge regression procedure for resistivity data inversion, Computer & Geosciences, 18, 99-118.
  • Nguyen, L.T., Nestorovic, T., 2016. Unscented hybrid simulated annealing for fast inversion of tunnel seismic waves, Comput. Methods Apll. Mech. Engrg., 301, 281-299.
  • Sen, M., Stoffa, P., 2013. Global optimization methods in geophysical inversion. Elsevier, Amsterdam.
  • Sharma, S.P., 2011. VFSARES- a very fast simulated annealing Fortran program for interpretation of 1-D DC resistivity sounding data from various electrode arrays, Computer and Geosciences, 42 (C), 177–188.
  • Slaoui, F.H., Georges, S., Lagace P.J., Do, X.D., 2003. The inverse problem of Schlumberger resistivity sounding measurement by ridge regression, Electric Power Systems Research, 67, 109-114. Telford, W.M., Geldart, L.P. and Sheriff, R.E., 1990. Applied geophysics, Cambridge University Press, Cambridge.
  • Tikhonov, A.N, Arsenin, VY 1977. Solutions of Ill-Posed Problems. New York, NY, USA: Halsted Press.
  • Zhdanov, M.S., Keller, G. V., 1994. The Geophysical Methods in Geophysical Exploration, Elsevier.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Geological Sciences and Engineering (Other)
Journal Section Research Articles
Authors

Kadir Kara 0000-0002-8899-3005

Ertan Pekşen 0000-0002-3515-1509

Publication Date March 30, 2021
Submission Date February 13, 2019
Acceptance Date December 29, 2020
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

APA Kara, K., & Pekşen, E. (2021). DOĞRU AKIM ÖZDİRENÇ VE MANYETOTELLÜRİK VERİLERİNİN TERS ÇÖZÜMÜ İÇİN MELEZ ALGORİTMA. Mühendislik Bilimleri Ve Tasarım Dergisi, 9(1), 77-87. https://doi.org/10.21923/jesd.526705