Отправлено: 26.06.14 05:17. Заголовок: Investigating 2-D MT inversion codes using real field data

Arabian Journal of Geosciences
June 2014, Volume 7, Issue 6, pp 2315-2328
Investigating 2-D MT inversion codes using real field data
Reza Ghaedrahmati, Ali Moradzadeh, Nader Fathianpour, Seong Kon Lee

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Abstract
There are currently a significant number of two-dimensional (2-D) and three-dimensional (3-D) inversion codes available for magnetotelluric (MT) data. Through various 2-D inversion algorithms suggested so far, the classical Occam's inversion, the data space Occam's inversion, the nonlinear conjugate gradient (NLCG) method, and the Gauss–Newton (GN) method are fundamental driving methods to find optimum earth models, and OCCAM, DASOCC, NLCG, and MT2DInvMatlab are possible candidates one can find in the public domain that implement these algorithms for 2-D MT inversions, respectively. In this study, we investigate the pros and cons (strength and weakness) of these codes to help one use them efficiently in practical works and, as an introductory guide, further develop (sophisticate or extend) them, especially for the 3-D case. To achieve this goal, we applied each one of the four aforementioned codes on a profile of real MT field dataset. Then, further investigations have been done by performing several inversion tests to see how each code can find the appropriate model to reconstruct the subsurface resistivity structure. Numerical experiments show that the two parameters, regularization and target misfit, in addition to the main criteria of inversion (such as the forward and the sensitivities calculation method, and the type of inversion algorithm), are very important to produce the expected model in inversion. The regularization parameter that acts to trade off between model norm and data misfit can affect the inversion process in terms of both the computational efficiency and the accuracy of the obtained model. Also, lack of insufficient precision to choose the target misfit can lead the inversion to produce and reach an incorrect model.