基于自适应模糊聚类分析的重力张量欧拉反褶积解

利用将重力全张量数据应用在欧拉反褶积中,规避位场梯度计算的精度问题,引入自适应模糊聚类算法克服聚类数目需要求预先确定、模糊聚类分析局部最优、分类不确定等弱点,并准确的确定多异常源的情况.核密度估计结果表明,张量欧拉反褶积比预设结构参数的欧拉反褶积方法更能表征地下异常类型；反演结果表明,传统欧拉反褶积难于识别在深大型异常源附近的浅部规模相对较小异常源；过滤后的欧拉反褶积解的空间包络基本与初始模型的一致,张量欧拉反褶积在获得多异常源的空间结构信息更具有优势.

Gravity tensor Euler Deconvolution solutions based on adaptive fuzzy cluster analysis

Tensor gravity Euler Deconvolution was used to avoid calculate Potential field’s derivatives.To accurately interpret the case with many causative sources.The adaptive fuzzy clustering analysis to overcome the drawbacks of fuzzy clustering analysis was used,such as(pre-determined number of clusters,local optimization,or classification uncertainty).Kernel density estimation results show that Tensor gravity Euler Deconvolution is a better estimate structural index of causative sources than traditional Euler Deconvolution.The inversion results show that Euler Deconvolution is difficult to identify small and shallow anomaly source,a relatively large-scale and deep anomaly source near it;the Euler solutions after filtered are coincident with the Forward model,which proves that the method of adaptive fuzzy clustering analyzing tensor gravity Euler Deconvolution is better than that analyzed by fuzzy clustering analyzing.