An adaptive regularized inversion algorithm for magnetotelluric data

作者：Chen XB^*; Zhao GZ; Tang J; Zhan Y; Wang JJ

来源：Chinese Journal of Geophysics, 2005, 48(4): 937-946.

• Adaptive Regularized Inversion Algorithm
• object function
• roughness kernel matrix
• magnetotellurics
• continuous medium

In this paper, a new inversion method, Adaptive Regularized Inversion Algorithm (ARIA), is presented to overcome the difficulty in determination of regularized factors for magnetotelluric (MT) inversion. In ARIA, first a new data variance disposing method, data variance normalization method, is put for-ward. This method uses a new way to calculate the influence matrix of data valiance in inversion. Thus, the data variance only influences data fitting, and has no influence on the weight between the data object function and the model constraint object function. So the influence factors in determination of the regularized factor are reduced. Second, the definition of roughness kernel matrix is presented in the course of constructing the model constraint object function, and a concise equation of it is derived. Thus the construction of the model object function becomes very simple and direct. Third two adaptive methods of regularized factors are put forward based on the relations of data object function, model constraint object function, and regularized factor. ARIA is used to solve the one-dimensional inversion by MT data by the constraint of the flattest model. Several examples are given to illustrate the effectiveness of ARIA.

• 出版日期2005-7
• 单位北京大学