Comprehensive Research On The Iterative Finite Element And The Improved Differential Evolution Of Magnetotelluric

This paper has made the improvement to the existing forward and inversion method of numerical simulation in magnetotelluric sounding. Based on the finite element method with iterative, the underground space of calculation has been divided into the target area(Inner area) and the iterative area(Out area). Iterative elimination has been used in the iterative area to add the energy of target area from the outside space to the boundary. Through this approach, it not only reduced the orders of finite element equations, but also ensured the boundary condition of the forward calculation, then saved the memory and improved the efficiency and accuracy of program. This algorithm has been equivalent to the finite combined with boundary element method. The iterative matrix computed only once in the target area has provided convenience for the subsequent inversion and improved the speed. According to the basic principle and characteristics of the iterative finite element algorithm, forward model has been established and then the correctness of the algorithm has been tested. Several important parameters and the influence of different parameters on the accuracy of the algorithm have also been analyzed.Differential evolution which was a global optimization algorithm in intelligent optimization has been introduced to the magnetotelluric inversion. This algorithm had high efficiency and strong robustness, and could avoid to calculate the partial derivative matrix in the inversion and to overlook the details information easily by making full use of the whole space parallel search in dealing with the optimization problem. Through the analysis of the standard differential evolution algorithm, the operators and integration have been improved for its easy to fall into local optimum in search process and the slow convergence. The dual operator strategy of dynamic integration improved the performance of the algorithm, and the improved algorithm has been tested by standard function. In addition, comparisons in performance of optimization have been made between TDE, standard differential evolution, simulated annealing and genetic algorithm. The results showed that the TDE method had the advantages of fast searching speed, high precision, good robustness and easy to escape from local optimum.On the basis of the above, combined with the regularization theory of magnetotelluric inversion, the reasonable objective function has been chosen and the nonlinear inversion model of good performance and intelligent optimization has been established. Iterative finite element modeling program and MT forward and inversion program of improved differential evolution algorithm have been prepared based on the Matlab platform, and the evaluation of the model based on the numerical simulation and field measured data has been validated. The verification results showed that the proposed intelligent optimization model with good inversion effect had the ability of field application.