TN572 : Two-dimensional inversion of magnetotelluric data in Gachsaran region using the smoothness-constrained least-squares method
Thesis > Central Library of Shahrood University > Mining, Petroleum & Geophysics Engineering > MSc > 2015
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Abstarct: Magnetotelluric method is a natural source electromagnetic method that being used in exploration of deep subsurface structures such as hydrocarbon structutres. The main objective of this investigation is two-dimensional modeling of Gachsaran MT data us-ing non linear conjugate gradient and smoothness-constrained least squares methods. These methods of inversion is implemented to identify the subsurface structures of this region including Sarab and Jafarabad anticlines, to delinate the upper boundary of Asmari Formation, to compare the results of these different inversion methods with together and finally the obtained results will be checked by geological information. To achieve the goal, the dimensionality analysis of subsurface structures was performed at the first step and the effects of local heterogeneities on the MT data were identi-fied, which were corrected using TEM sounding data, then the corrected MT data were inverted for exploration of subsurface targets. The results of this study indicate that the subsurface structures are generally 2D and 3D and it has been found taht the modeling of MT data for TM+TE mode has a better application in comparison with other modes. Moreover the results indicate that between non linear conjugate gradient and smoothness-constrained least squares modeling methods, nonlinear conjugate gra-dient inversion method provides better results which are in good agreement with geo-logical information.obtained results of inversion has identidfied surface structures of the region and upper boundary of Asmari Formation precisely. Moreover Mountain Front Fault was detected in this study. Approximation location of the Sarab anticline was obtained at a depth of 1500 meters below sea level.
Keywords:
#magnetotelluric #inversion #smoothness-constrained least squares #nonlinear conjugate gradient #Gachsaran
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: