地下目标体重力张量数据的边界识别与增强算法的研究

边界增强与识别在重力数据处理中占据重要地位,与传统重力异常数据相比,重力张量及其高阶分量对于直接反映异常体的边界具有更高的精度。当异常数据中的所有网格点的值均较低时,通过Sigmoid变换,可以实现高异常值网格数据的拉升,同时压缩低灰度级像素,从而凸显地质体边界,提高边界增强后图像的识别效果。文中利用张量及其分量构建常用的边界识别算法,通过组合体模型进行多种边界识别算法的试算,以比较分析各自的效果,并对结果进行Sigmoid变换。结果表明:对于张量高阶分量组合形式,水平梯度模、解析信号能基本反映浅异常体的边界,gzz水平梯度模能较好反映浅异常体边界,但三者均不能识别深异常体边界;Tilt梯度、Theta和ITA3效果不理想;ITA2能在有效均衡不同强度异常信号的同时,清晰地识别不同深度异常体的边界;采用Sigmoid变换,可以明显提高边界识别的识别效果。

Research on underground anomaly boundary recognition and enhancement algorithms based on gravitational tensor

Boundary enhancement and recognition occupies the important position in magnetic data processing.Compared with the traditional gravity anomaly data,gravitational tensor data has a higher accuracy in directly reflecting the boundary of the anomaly.High abnormal values can be pulled up with low gray-scale pixels compressed at the same time by Sigmoid transformation,when all the values of the grid points are lower,thus highlighting geological boundary and enhancing image recognition effect.This paper calculates many kinds of gravity anomaly processing algorithms based on tensor components, including Sigmoid transformation,and then analyzes their effects through combination model.The result shows that:the horizontal gradient,analytic signal and the horizontal gradient of,gzz can reflect the boundary of the shallow anomaly with the horizontal gradient of,gzz a better effect,but none of them can recognize the boundary of deep anomaly.The result of Tilt gradient and Theta diagram method is not ideal,so it is with the ITA3 .By contrast,the ITA2 can effectively balance the different intensity anomaly and at the same time clearly identify the different depth of anomaly boundary.Sigmoid transformation is proposed,which can distinctly improve the recognition effect of boundary identification.