Application of K-means++Partition Method in Intensive Stations

GAMIT software generally needs to perform partition processing when solving large-scale intensive stations. Partition resolution has a certain influence on the accuracy of the results. In order to solve the problem that the long-short baselines exist in the general partitioning method and the accuracy of the whole network solution is reduced, the K-means++algorithm and the Hash algorithm are introduced to implement partitioning, which is referred to as the K-means++partition method. First, using K-means++algorithm to cluster stations, and then using Hash algorithm to sort and combine, so that we can obtain an uniform distribution subnet. In this paper, the result of the whole network solution is used as the standard value. The baseline length, baseline accuracy and three-dimensional coordinate difference of the regional zoning method and the K-means++zoning method are analyzed. Then the K-means++partition method and the spacing zoning method are compared and analyzed. The experimental results show that this method is more accurate than the regional zoning method, and it is in line with the accuracy of the existing spacing zoning method, and is more stable and efficient than the zoning method.