田宇, 柯小平, 王勇. 利用航空重力梯度反演Kauring试验场三维密度结构[J]. 武汉大学学报 ( 信息科学版), 2019, 44(4): 501-509. DOI: 10.13203/j.whugis20160503
引用本文: 田宇, 柯小平, 王勇. 利用航空重力梯度反演Kauring试验场三维密度结构[J]. 武汉大学学报 ( 信息科学版), 2019, 44(4): 501-509. DOI: 10.13203/j.whugis20160503
TIAN Yu, KE Xiaoping, WANG Yong. Inversion of Three-Dimensional Density Structure Using Airborne Gradiometry Data in Kauring Test Site[J]. Geomatics and Information Science of Wuhan University, 2019, 44(4): 501-509. DOI: 10.13203/j.whugis20160503
Citation: TIAN Yu, KE Xiaoping, WANG Yong. Inversion of Three-Dimensional Density Structure Using Airborne Gradiometry Data in Kauring Test Site[J]. Geomatics and Information Science of Wuhan University, 2019, 44(4): 501-509. DOI: 10.13203/j.whugis20160503

利用航空重力梯度反演Kauring试验场三维密度结构

Inversion of Three-Dimensional Density Structure Using Airborne Gradiometry Data in Kauring Test Site

  • 摘要: 相对于传统的重力测量手段,重力梯度测量能够以更高的灵敏度和分辨能力反映出地下密度异常体的结构特征。由于拉格朗日经验参数在实测数据反演中存在不确定性,对预条件共轭梯度反演算法加以改进,利用L曲线的拐点值代替原反演算法中的拉格朗日经验参数作为正则化参数;为改善反演中存在的病态性问题并减弱核函数的快速衰减,将地下模型改进为不等间隔模型;为改善反演中解的非唯一性,利用重力梯度的5个独立分量进行联合反演;通过对澳大利亚Kauring试验场航空重力梯度张量进行联合反演,得到该地区异常体的三维密度分布,将重力梯度联合反演结果与之前的重力反演结果对比分析,发现在中心异常体附近沿线还分布着多个异常块体。结果表明,改进后的算法能够有效地利用实测重力梯度数据反演出密度异常体的分布信息。

     

    Abstract: Due to the uncertainty of the Lagrange empirical parameter, selecting empirical of parameters for diverse observed data sets introduces uncertainty into the results, which weakens the applicability of the inversion method. By using the turning point of the L curve to replace the Lagrange empirical parameter as the regularization parameter, the algorithm focusing on the preconditioned conjugate gradient algorithm has been improved. The underground models have been converted to models with unequally spaced aiming to solve ill conditioned problem as to well as weaken kernel function attenuation. In order to take full advantage of the gravity gradient multiple components, the method of joint five independent measured components of tensor gradient gravity data has been taken with the purpose of meliorating the non uniqueness of inversion results. The effectiveness and reliability of the improved method are validated by the statistical analysis of multiple sets of synthetic models. For the application of the field data, analysis result shows that the improved calculation method is effectively applicable to the inversion of measured gravity gradient data, through inversion of airborne gradiometry data on Australian Kauring test site, we obtained 3D distribution of underground density anomalies. According to the previous results of gravity data inversion, this paper verifies the effectiveness of the algorithm, and discovers more anomaly blocks besides the central anomaly blocks. Our results show that the improved algorithm using field measurements can inverse the distribution of density anomalies, and the inversion results provide more detailed and reliable pattern information for the density anomaly.

     

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