PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

An effective estimate for selecting the regularization parameter in the 3D inversion of magnetotelluric data

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The magnetotelluric (MT) inverse problem is a nonlinear and strongly ill-posed problem. Therefore, to avoid the problem of non-uniqueness of response, this problem is mainly solved by Tikhonov regularization method. The purpose of this study is to present a suitable method for selecting the regularization parameters in the 3D MT inverse problem, with regard to the accuracy and speed of the inversion. In this research, the regularization parameter is simply estimated in each iteration of inversion as the ratio of the data misfit to sum of the data misfit and model norm in the pre-iteration. This scheme is applied in the well-known 3D inversion algorithm, WSInv3DMT, instead of the discrepancy principle method. The accuracy of this scheme is assessed by performing the inversion on synthetic models and real data. Results from the inversion for the synthetic and real data indicate that the data misfit and the model norm are reduced with an acceptable rate during the inversion operation. The inverse model has been smoothly converged to an appropriate model and that unrealistic structures have not been included in the model. The results also show that estimation of the regularization parameter by the discrepancy principle method and continuing the inversion to achieve the target data misfit may lead to the production of a model with non-realistic structures, while in the proposed scheme the inversion has not encountered this problem and it converges to an appropriate model after fewer iterations of inversion. In addition, the results show that the time consumed for the inversion of a set of real data with 41 stations and 16 measurement frequencies would decrease up to 27 percent compared to the time devoted for inverting the same set of data by the discrepancy principle method. Also the inversion does not deviate toward unrealistic models and it closely converges to the model of real geological structures.
Czasopismo
Rocznik
Strony
609--621
Opis fizyczny
Bibliogr. 31 poz.
Twórcy
  • Mining Department, Engineering Faculty, Lorestan University, Khorramabad, Iran
  • School of Mining Engineering, College of Engineering, Tehran University, Tehran, Iran
  • Mining Department, Engineering Faculty, Lorestan University, Khorramabad, Iran
Bibliografia
  • 1. Bauer F, Kindermann S (2008) Recent results on the quasi-optimality principle. J Inverse Ill-Posed Prob 17:5–18
  • 2. Bauer F, Lukas MA (2011) Comparing parameter choice methods for regularization of ill-posed problems. Math Comput Simul 81(9):1795–1841
  • 3. Constable SC, Parker RL, Constable CG (1987) Occam’s inversion: a practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics 52:289–300
  • 4. deGroot-Hedlin C, Constable S (1990) Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics 55:1613–1624
  • 5. Farquharson CG, Oldenburg DW (2004) A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems. Geophy J Int 156:411–425
  • 6. Ghaedrahmati R, Moradzadeh A, Fathianpour N, Lee S (2014a) An improvement of 2-D inversion of MT data using automatic selection methods for regularization parameter. Iranian Geophys J 9(1):30–45
  • 7. Ghaedrahmati R, Moradzadeh A, Fathianpour N, Lee SK, Porkhial S (2013) 3-D inversion of MT data from the Sabalan geothermal field, Ardabil, Iran. J Appl Geophys 93:12–24
  • 8. Ghaedrahmati R, Moradzadeh A, Fathianpour N, Lee SK (2014b) Investigating 2-D MT inversion codes using real field data. Arab J Geosci 7:2315–2328
  • 9. Haber E (1997) Numerical strategies for the solution of inverse problems. Ph.D Thesis, University of British Columbia, Vancouver, Canada
  • 10. Haber E, Oldenburg DW (2000) A GCV based method for nonlinear ill-posed problems. Comput Geosci 4:41–63
  • 11. Hadamard J (1923) Lectures on Cauchy’s problem in linear partial differential equations. Yale University Press, New Haven
  • 12. Hansen PC (1997) Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion. SIAM, Philadelphia
  • 13. Kaltenbacher B, Kirchner A, Vexler B (2011) Adaptive discretizations for the choice of a Tikhonov regularization parameter in nonlinear inverse problems. Inv Probl 27:125008 (28 pp)
  • 14. Lee SK, Kim HJ, Song Y, Lee C (2009) MT2DInvMatlab—a program in MATLAB and FORTRAN for two-dimensional magnetotelluric inversion. Comput Geosci 35:1722–1735
  • 15. Li Y, Oldenburg DW (1999) 3-D inversion of DC resistivity data using an L-curve criterion. In: 69th Annual international meeting of the SEG, expanded abstracts, pp 251–254
  • 16. Mitsuhata Y, Uchida T, Amano H (2002) 2.5-D inversion of frequency domain electromagnetic data generated by a grounded-wire source. Geophysics 67:1753–1768
  • 17. Newman GA, Alumbaugh DL (2000) Three dimensional magnetotelluric inversion using non-linear conjugate gradients. Geophys J Int 140:410–424
  • 18. Pellerin L, Johnston JM, Hohmann GW (1996) A numerical evaluation of electromagnetic methods in geothermal exploration. Geophysics 61(1):121–130
  • 19. Rodi WL, Mackie RL (2001) Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion. Geophysics 66:174–187
  • 20. Sasaki Y (2004) Three-dimensional inversion of static-shifted magnetotelluric data. Earth Planets Space 56:239–248
  • 21. Siripunvaraporn W, Egbert G (2000) An efficient data-sub space inversion method for 2-D magnetotelluric data. Geophysics 65:791–803
  • 22. Siripunvaraporn W, Egbert G, Lenbury Y, Uyeshima M (2005) Three-dimensional magnetotelluric inversion: data-space method. Phys Earth Planet Inter 150(1–3):3–14
  • 23. Smith JT, Booker JR (1991) Rapid relaxation inversion of two- and three-dimensional magnetotelluric data. J Geophys Res 96:3905–3922
  • 24. Smith JT, Booker JR (1988) Magnetotelluric inversion for minimum structure. Geophysics 53:1565–1576
  • 25. Tikhonov AN, Arsenin VY (1977) Solution of ill-posed problems. V. H. Winston and Sons, 258 pp
  • 26. Uchida T (1993) Smooth 2-D Inversion for Magnetotelluric data based on statistical Criterion ABIC. J Geomag Geoelectr 45:841–858
  • 27. Walker SE (1999) Inversion of EM data to recover 1-D conductivity and a geometric survey parameter. MSc thesis, University of British Columbia
  • 28. Xiang Y, Yu P, Zhang L, Feng S, Utada H (2017) Regularized magnetotelluric inversion based on a minimum support gradient stabilizing functional. Earth Planets Space 69:2–18
  • 29. Xiao Q, Cai X, Xu X, Liang G, Zhang B (2010) Application of the 3D magnetotelluric inversion code in a geologically complex area. Geophys Prospect 58(6):1177–1192
  • 30. Zhdanov MS (2002) Geophysical inverse theory and regularization problems. Elsevier, Amsterdam, London, New York, Tokyo, p 628
  • 31. Zhdanov MS, Wan L, Gribenko A, Čuma M, Key K, Constable S (2011) Large-scale 3D inversion of marine magnetotelluric data: case study from the Gemini prospect, Gulf of Mexico. Geophysics 76(1):77–87
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-effad94e-88f1-4a5a-a6e9-db96e7b5bab5
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.