Abstract:We are concerned with the applicability and selection of different remove-restore elevation conversion models in the process of quasi geoid refinement. We construct RBF neural networks, polyhedral function and Shepard function based on quadratic surfaces, and an EGM2008 gravity field model. We apply these tools to two engineering examples in the plain and plateau mountains, in order to fit normal height and compare the accuracy of each model by adjusting the number of fitting points. Experiments show that in the plain area, the accuracy of the EGM2008-polyhedral functions elevation model is slightly better than the other five models; in the plateau mountainous area, when the number of fitting points is fewer, the accuracy of the remove-restore models based on EGM2008 is higher than the corresponding models based on quadratic surfaces, and the EGM2008- polyhedral function model and the EGM2008-Shepard model are superior. As the number of fitting points increases, the accuracy of the quadratic surfaces-Shepard transformation model is better than the other five models.
LI Mingfei,WU Junchao,QIN Chuan. Analysis and Comparison of Local Quasi Geoid Refinement Model Based on Remove-Restore Method[J]. jgg, 2020, 40(9): 952-956.