Two-dimensional inversion of magnetotelluric data with consecutive use of conjugate gradient and least-squares solution with singular value decomposition algorithms

摘要

I investigated the two-dimensional magnetotelluric data inversion algorithms in studying two significant aspects within a linearized inversion approach. The first one is the method of minimization and second one is the type of stabilizing functional used in parametric functionals. The results of two well-known inversion algorithms, namely conjugate gradient and the least-squares solution with singular value decomposition, were compared in terms of accuracy and CPU time. In addition, magnetotelluric data inversion with various stabilizers, such as L2-norm, smoothing, minimum support, minimum gradient support and first-order minimum entropy, were examined. A new inversion algorithm named least-squares solution with singular value decomposition and conjugate gradient is suggested in seeing the outcomes of the comparisons carried out on least-squares solutions with singular value decomposition and conjugate gradient algorithms subject to a variety of stabilizers. Inversion results of synthetic data showed that the newly suggested algorithm yields better results than those of the individual implementations of conjugate gradient and least-squares solution with singular value decomposition algorithms. The suggested algorithm and the above-mentioned algorithms inversion results for the field data collected along a line crossing the North Anatolian Fault zone were also compared each other and results are discussed.