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.

紧凑重力反演（CGI）由于其简单，高效和锐利边界反演结果而遵循最小化病因体体积的原理，被广泛用于对重力数据进行反演。在这项研究中，对紧凑性加权函数进行了归纳，并将深度加权函数引入CGI，以获得重新加权的CGI（RCGI）方法。尽管RCGI具有比CGI更好的灵活性，但是选择合适的紧密度因子α和深度加权函数β是困难的，并且我们设计了一个参数选择规则来搜索合适的\（\ alpha \）和\（\ beta \）定量地。此外，我们通过逐步消除在迭代反演算法中达到指定边界的模型块，提高了RCGI的计算效率。这种方法称为重加权和元素消除CGI（REECGI）方法。反演结果表明，RCGI和REECGI均比CGI产生更好的反演精度，并且REECGI的计算效率比RCGI和CGI高，并且随着迭代次数的增加而增加。