### Refine

#### Document Type

- Preprint (4) (remove)

#### Language

- English (4)

#### Has Fulltext

- yes (4) (remove)

#### Is part of the Bibliography

- no (4)

#### Keywords

- Abel integral equations (1)
- Gaussian random noise (1)
- Inverse problems in Banach spaces (1)
- Inverses Problem (1)
- Lavrentiev regularization for equations with monotone operators (1)
- Regularisierung (1)
- Regularization (1)
- Satellitengradiogravimetrie (1)
- Weißes Rauschen (1)
- parameter choice (1)
- satellite gravity gradiometry (1)
- severely ill-posed inverse problems (1)

#### Faculty / Organisational entity

In this paper we discuss a special class of regularization methods for solving the satellite gravity gradiometry problem in a spherical framework based on band-limited spherical regularization wavelets. Considering such wavelets as a reesult of a combination of some regularization methods with Galerkin discretization based on the spherical harmonic system we obtain the error estimates of regularized solutions as well as the estimates for regularization parameters and parameters of band-limitation.

We study a possiblity to use the structure of the regularization error for a posteriori choice of the regularization parameter. As a result, a rather general form of a selection criterion is proposed, and its relation to the heuristical quasi-optimality principle of Tikhonov and Glasko (1964), and to an adaptation scheme proposed in a statistical context by Lepskii (1990), is discussed. The advantages of the proposed criterion are illustrated by using such examples as self-regularization of the trapezoidal rule for noisy Abel-type integral equations, Lavrentiev regularization for non-linear ill-posed problems and an inverse problem of the two-dimensional profile reconstruction.

The mathematical formulation of many physical problems results in the task of inverting a compact operator. The only known sensible solution technique is regularization which poses a severe problem in itself. Classically one dealt with deterministic noise models and required both the knowledge of smoothness of the solution function and the overall error behavior. We will show that we can guarantee an asymptotically optimal regularization for a physically motivated noise model under no assumptions for the smoothness and rather weak assumptions on the noise behavior which can mostly obtained out of two input data sets. An application to the determination of the gravitational field out of satellite data will be shown.