Abstract:The regularized methods are widely used in geophysical inversion to improve the ill-posedness of inverse problem. The selection of the regularization parameter is very important to the results of regularized inversion, most of the recent researches usually concern about a single way such as a fixed value of regularization parameter. The research on how to choose the optimal regularization parameter is uncertain. In this paper, by establishing layered magnetotelluric (MT) models, the conjugate gradient method is used in Occam’s inversion, we compare the inversion results of many ways to select the optimal regularization parameter with different initial model in order to analysis the characteristic and some conditions of each methods. The results show that the adaptive regularization algorithm can get a similar inversion result compared with some traditional ways such as L-curve method, and it’s faster and easier to use. Moreover, with the purpose to reduce the limit of inversion depending on the initial model and improve the stability of inversion, we put forward an improved adaptive regularization method which selects a large ratio of the misfit functional and stabilizing functional as the initial value of regularization parameter, takes an automatic decay coefficient scheme to reduce the regularization parameter when misfit functional decline slowly. Model test shows that the method is much lower depended on initial model than other adaptive algorithms, the inversion result is stable and it can be further improved the efficiency of regularization inversion.
