Analysis of gravity gradiometer inverse problems using optimal design measures

Abstract

Tensor components provide related but independent measures of the gravity field. In commercial systems, some of the components are measured directly while others are calculated. With five independent tensor components available for interpretation, the question of which components to use arises. Using ideas from optimal survey design, we investigated the information content provided by each of the tensor components and combinations thereof. The main tool used is the singular value decomposition of the design matrix resulting from posing the 3D inversion problem of determining densities from gravity gradiometer data. Optimal design measures suggest that at larger measurement-source distances all components and combinations provide similar levels of information concerning the source density distribution. Simply adding more components to the inverted data set does not provide any advantages. This changes at smaller measurement-source distances where multicomponent combinations provide stronger information content and [Formula: see text] is the single tensor component with the largest information value.