A regularized three-dimensional magnetotelluric inversion with a minimum gradient support constraint

SUMMARY Most 3-D magnetotelluric (MT) inversions are classified as a regularized inversion with a smoothness constraint. These inverse algorithms provide smooth solutions but cannot clearly image sharp geo-electrical interfaces. In this paper, we introduce the minimum gradient support (MGS) functional to regularize the 3-D MT inverse problem. This functional has a property whereby the functional seeks a structure with minimum volume containing large conductivity gradients. Therefore, the MGS functional can be used to search for a model with a sharp boundary. We apply the MGS functional to 3-D MT inversion to obtain a clear and accurate image of geo-electrical interfaces. In addition, the modified scattering equation approach introduced in the modified iterative dissipative method (MIDM) is applied to forward calculation, which is based on integral equation (IE) formulation and allows us to efficiently reduce the time required for forward calculation with high accuracy. The quasi-Newton iterative method is used to optimize the objective functional. It is a kind of iterative method with simplified calculation of the inverse Hessian matrix using Broyden–Fletcher–Goldfarb–Shanno (BFGS) update. The convergence of this iterative method is guaranteed with inexact line searches. We also modify the adaptive approach for optimum selection of the regularization parameter so as to fit the inverse algorithm of this study. Three synthetic models are investigated, and the obtained results are compared with those obtained by a smoothing inversion. Based on the comparison, we confirm that the MGS inversion can provide higher resolution when geo-electrical interfaces are sharp. This property will help us to determine reliable electrical structures by the MT exploration method.

[1]  Y. Sasaki Full 3-D inversion of electromagnetic data on PC , 2001 .

[2]  B. Sh. Singer,et al.  Method for solution of Maxwell's equations in non-uniform media , 1995 .

[3]  K. Kubik,et al.  Compact gravity inversion , 1983 .

[4]  Michael S. Zhdanov,et al.  Three-dimensional regularized focusing inversion of gravity gradient tensor component data , 2004 .

[5]  S. Hautot,et al.  3‐D magnetotelluric inversion and model validation with gravity data for the investigation of flood basalts and associated volcanic rifted margins , 2007 .

[6]  S. Constable,et al.  Occam's inversion to generate smooth, two-dimensional models from magnetotelluric data , 1990 .

[7]  Gregory A. Newman,et al.  High-Performance Three-Dimensional Electromagnetic Modelling Using Modified Neumann Series. Wide-Band Numerical Solution and Examples , 1997 .

[8]  Gerald W. Hohmann,et al.  Electromagnetic modeling of three-dimensional bodies in layered earths using integral equations , 1983 .

[9]  V. Dmitriev,et al.  Quasi-analytical approximations and series in electromagnetic modeling , 2000 .

[10]  R. Fletcher,et al.  A New Approach to Variable Metric Algorithms , 1970, Comput. J..

[11]  G. Newman,et al.  Three-dimensional magnetotelluric inversion using non-linear conjugate gradients , 2000 .

[12]  Dmitry B. Avdeev,et al.  3D magnetotelluric inversion using a limited-memory quasi-Newton optimization , 2009 .

[13]  M. Zhdanov,et al.  3‐D magnetic inversion with data compression and image focusing , 2002 .

[14]  Michael S. Zhdanov,et al.  Focusing geophysical inversion images , 1999 .

[15]  C. M. Swift,et al.  On determining electrical characteristics of the deep layers of the Earth's crust , 1986 .

[16]  V. Spichak,et al.  Artificial neural network inversion of magnetotelluric data in terms of three‐dimensional earth macroparameters , 2000 .

[17]  E. Fainberg,et al.  Modelling of electromagnetic fields in thin heterogeneous layers with application to field generation by volcanoes—theory and example , 1999 .

[18]  R. Parker,et al.  Occam's inversion; a practical algorithm for generating smooth models from electromagnetic sounding data , 1987 .

[19]  L. Cagniard Basic theory of the magneto-telluric method of geophysical prospecting , 1953 .

[20]  Michael S. Zhdanov,et al.  Electromagnetic inversion using quasi-linear approximation , 2000 .

[21]  Shao-Liang Zhang,et al.  GPBi-CG: Generalized Product-type Methods Based on Bi-CG for Solving Nonsymmetric Linear Systems , 1997, SIAM J. Sci. Comput..

[22]  Yongwimon Lenbury,et al.  Three-dimensional magnetotelluric inversion : data-space method , 2005 .

[23]  R. Mackie,et al.  Three-dimensional magnetotelluric inversion using conjugate gradients , 1993 .

[24]  Hisayoshi Shimizu,et al.  Possible effects of lateral heterogeneity in the D″ layer on electromagnetic variations of core origin , 2002 .

[25]  Luolei Zhang,et al.  Smoothest Model and Sharp Boundary Based Two‐Dimensional Magnetotelluric Inversion , 2009 .

[26]  J. T. Smith,et al.  Rapid inversion of two‐ and three‐dimensional magnetotelluric data , 1991 .

[27]  Nils Olsen,et al.  Chapter 3 Modelling electromagnetic fields in a 3D spherical earth using a fast integral equation approach , 2002 .

[28]  Michael S. Zhdanov,et al.  New advances in regularized inversion of gravity and electromagnetic data , 2009 .

[29]  Gregory A. Newman,et al.  Three‐dimensional induction logging problems, Part I: An integral equation solution and model comparisons , 2002 .

[30]  A. Kuvshinov,et al.  Electromagnetic field scattering in a heterogeneous Earth: A solution to the forward problem , 1995 .