Generalisation and improvement of the compact gravity inversion method

Abstract

Compact gravity inversion (CGI) is widely used to invert gravity data following the principle of minimising the volume of the causative body due to its simplicity, high efficiency, and sharp-boundary inversion results. In this study, the compactness weighting function is generalised and the depth weighting function is introduced to CGI to obtain the reweighted CGI (RCGI) method. Although RCGI exhibits better flexibility than CGI, selecting an appropriate compactness factor α and depth weighting function β is difficult, and we design a parameter selection rule to search the proper $$\alpha$$ and $$\beta$$ quantitively. Furthermore, we improve RCGI for boasting superior computational efficiency by gradually eliminating the model blocks that reach the designated boundaries in the iterative algorithm of inversion. This approach is termed the reweighted and element-elimination CGI (REECGI) method. The inversion results show that both RCGI and REECGI result in better inversion accuracy than CGI, and REECGI has higher computational efficiency than RCGI and CGI, which increases with the number of iterations.