Sequential tikhonov regularization: An alternative way for integral inversion of satellite gradiometric data

摘要

Numerous regularization methods exist for solving the ill-posed problem of downward continuation of satellite gravity gradiometry (SGG) data to gravity anomaly at sea level. Generally, the use of a dense set of data is recommended in the downward continuation. However, when such dense data are used some of the regularization methods are not efficient and applicable. In this paper, a sequential way of using the Tikhonov regularization is developed for solving large systems and compared to methods of direct truncated singular value decomposition and iterative methods of range restricted minimum residual, algebraic reconstruction technique, v and conjugate gradient for recovering gravity anomaly at sea level from the SGG data. Numerical studies show that the sequential Tikhonov regularization is comparable to the conjugate gradient and yields similar result.