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本文利用Extrapolation Tikhonov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,Extrapolation Tikhonov正则化方法减小了因正则化参数的引入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。
In this paper, the extrapolation Tikhonov regularization algorithm is used to deal with the linear ill-posed problem of gravity density data inversion. Compared with the Tikhonov regularization method, the extrapolation Tikhonov regularization method reduces the error of the inversion results brought by the introduction of the regularization parameter, and improves the fitting accuracy between the prediction data and the observation data. At the same time, in order to eliminate the effect of fast attenuation of the position function on the inversion results when the field data is back-ground, a depth weighting function based on the gravitational gradient total tensor eigenvector is proposed in this paper. The model test proves that the depth weighting function can effectively identify the abnormal body Density distribution characteristics. The gravity gradient data of the measured Kauring region in Australia were inverted and compared with the existing research results. The results show that the inversion method can obtain the density distribution information of underground anomalies well.