中文摘要：

本文利用ExtrapolationTikhonov正则化算法处理重力梯度数据三维密度反演的线性不适定问题。与Tikhonov正则化方法相比，ExtrapolationTikhonov正则化方法减小了因正则化参数的引人而带来的反演结果误差，提高了预测数据与观测数据之间的拟合精度。同时为了消除位场数据反演时位置函数快速衰减对反演结果的影响，本文提出了基于重力梯度全张量特征向量法的深度加权函数，模型试验证明了该深度加权函数能有效识别异常体密度分布特征。对澳大利亚Kauring地区实测重力梯度数据进行反演，并和已有研究成果对比分析。结果表明该反演方法能够较好的获取地下异常体的密度分布信息。

英文摘要：

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.