应用聚类分析方法进行实测重力数据的选点优化

Optimization of Gravimetric Data Positions for Computation Local Geoid by Clustering Analysis

针对局部(似)大地水准面的求解过程,将聚类分析方法用于重力观测数据的优化设计。根据重力场的变化特征,利用地形的双向坡度值作为分类属性,给出了实测重力数据可以较稀疏或必须稠密的判断依据。在一处丘陵地区进行了数值实验,结果表明,应用此方法在非均匀地删除掉近一半的实测重力数据之后,计算得到的(似)大地水准面变化的最大值为1.2 cm,最小值为-0.4 cm,平均值为0.3 cm,与未删除实测重力数据情况下获得的计算结果精度相当。由此验证了该方法的可行性,并为局部(似)大地水准面求解过程的优化设计提供了一条可借鉴的途径。

A clustering analysis method is used in the optimization of observed gravity data during the computation of local geoid. Two slopes of terrain data were used as criteria in order to find which data is more important according to the characteristics of gravity field. A numerical experiment was carried out in hill area. After half observed gravity data are deleted, the maximum variation of the geoid is 1.2 cm, the minimum is --0.4 cm, and the average variation is 0.3 cm. Compared with the result from non-deleted observed gravity data, the deleted data still works out acceptable results of similar precision. The numerical experiment validates the feasibility of the clustering analysis methods, which provides a new approach to optimize observed gravity data for computing local geoid.