Application of real coded genetic algorithm in two-dimensional magnetotelluric inversion
实数编码遗传算法在大地电磁测深二维反演中的应用

Genetic algorithm, one of the new methods for global non-linear optimization, has been applied to magnetotelluric sounding (MT) data analysis. But the magnetotelluric inverse problem is ill-posed, and therefore unstable and non-unique. In this paper, the inverse problem of two-dimensional magnetotelluric has been solved by using a real coded genetic algorithm that employing Tikhonov regularization method. The approach is based on regularization theory and genetic algorithm is utilized for searching for the minimum of the parametric functional. The normal optimum inversion methods based on local linearization are usually lost in local minimum values, and they seriously depend on the selection of the initial model. Compared with the traditional iterative inversion methods through linearization, the real coded GA is able to overcome disadvantages of the traditional inversion and obtain better results. The inversion results of magnetotelluric sounding synthetic models are ideal, which indicates that the algorithm possesses advantages of expediting convergence, avoiding earliness and improving precision. The real coded GA is highly adaptable and well suited to non-linear hypothesis testing as well as to inverse modeling. So it can be used in magnetotelluric sounding data analysis.