Optimal selection of regularization parameter in inversion of magnetotelluric data

Authors

Abstract

The essential issue in the interpretation of magnetotelluric (MT) field data is the solutions for inverse problems that should be in agreement with realistic subsurface geological structures. The MT data inverse modeling is known as an ill posed and nonlinear inverse problem, therefore, in order to prevent the problem of nonunique solution and to obtain meaningful results, it is normally solved using Tikhonov regularization method as the most popular regularization method. Another crucial problem in geophysical data inversion particularly MT field data is to reduce computation time and memory requirements. For achieving more precise inversion solutions, selection of the optimal regularization parameter value is an important factor in the inversion process. The novelty of the present study is to propose a new two-dimensional (2D) algorithm that has been developed for smooth inversion of MT data in which the optimal regularization parameter has been considered. In order to achieve this objective, the forward solution and Lanczos bidiagonalization (LB) algorithm have been implemented in a MATLAB code. For finding an appropriate value for the regularization parameter, adaptive regularization has been used and compared with modified generalized cross-validation (MGCV) and active constraint balancing (ACB) methods. The generated MT data by a 2D synthetic model and Bushli (Nir) geothermal MT field data in Ardabil Province, Iran, have been used in the inversion by the proposed algorithm. The results indicate that the proposed method is more effective and faster than the compared methods especially in less memory usage, decreasing elapsed time, and more accurate solution in the inversion process.

Keywords

References

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Volume 7, Issue 3
January 0
Pages 253-265

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