基于深度加权的多分量重力梯度数据联合相关成像方法

针对重力勘探中相关成像存在纵向分辨率较低的问题，提出基于深度加权的多分量重力梯度数据联合相关成像方法。与重力异常数据相比，重力梯度数据具有更高的信噪比、包含更多的频率信息。因此，本文在相关成像原理基础上联合多分量重力梯度数据，引入基于先验信息的深度加权函数，通过对研究区域进行划分，进一步提高深度加权效果。利用长方体组合理论模型确定了最佳梯度数据组合，验证了深度加权函数能够提高纵向成像效果；提出的方法还被证明具有抗噪性。将该方法应用于文顿盐丘区域的实测重力梯度数据，结果能够清晰地显示盖岩的位置。

Correlation Imaging Method with Joint Multiple Gravity Gradiometry Data Based on Depth Weighting

Aiming at the problem of low vertical resolution of correlation imaging in gravity exploration, the authors propose a correlation imaging method with joint multiple gravity gradiometry data based on depth weighting. Compared with gravity anomaly, the gravity gradiometry data have a higher signal-to-noise ratio, and contain more frequency information. Combined with multiple tensors of gravity gradiometry data, based on correlation imaging principle, a depth weighting function is introduced with the prior information. Using the theoretical model of the prism combination, the optimal gradiometry data group is determined, and the improvement of the vertical imaging results is verified by depth weighting function. Through dividing research area, the effect of depth weighting is further improved. The proposed method is also proved to be anti-noised. The method is applied to the measured gravity gradiometry data of Vinton Dome, and the results clearly show the location of the cap rock.