DEFENITION OF APPROPRIATE GEODETIC DATUM USING ROBUST STATISTICAL METHODS

被引:1
|
作者
Marjetic, Ales [1 ]
Kregar, Klemen [1 ]
机构
[1] Univ Ljubljani, Fak Gradbenistvo & Geodezijo, Jamova Cesta 2, SI-1000 Ljubljana, Slovenia
关键词
geodetic datum; S transformation; robust statistic; displacement; influence function; IWP - iterative weighted projection;
D O I
10.15292/geodetski-vestnik.2016.02.212-226
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
The correct determination of geodetic datum is an obligatory condition for the proper determination of the point displacements. There are various methods of deformation analysis. focused on the right identification of stable points, which nay, define an appropriate coordinate basis for calculating the displacements of other points. These methods' are largely based on the statistical testing and represent a comprehensive and complex analysis of change of geometry of geodetic network and allow definition of statistically significant displacements. Knowing the characteristics of the transformation between the solutions of displacements that are based or different definitions of geodetic datums, the problem defining an appropriate geodetic datum can be solved in a slightly different way. In this article we have focused on the problem of determining the appropriate weighting matrix E in the model of S-transformation. We used the generally known methods of robust statistics. The robustness of the three selected methods were tested on two different situations of preselected displacements in the considered geodetic network and the results on selected case of geoetic network with results of conventional method, of deformation analysis were compared.
引用
收藏
页码:212 / 226
页数:15
相关论文
共 50 条
  • [31] Robust Statistical Methods for Empirical Software Engineering
    Barbara Kitchenham
    Lech Madeyski
    David Budgen
    Jacky Keung
    Pearl Brereton
    Stuart Charters
    Shirley Gibbs
    Amnart Pohthong
    Empirical Software Engineering, 2017, 22 : 579 - 630
  • [32] Robust statistical methods for exclusive hypothesis test
    Li, Meng
    Sun, Jianguo
    Tong, Xingwei
    STATISTICS AND ITS INTERFACE, 2025, 18 (01) : 81 - 92
  • [33] Robust Statistical Methods for Empirical Software Engineering
    Kitchenham, Barbara
    Madeyski, Lech
    Budgen, David
    Keung, Jacky
    Brereton, Pearl
    Charters, Stuart
    Gibbs, Shirley
    Pohthong, Amnart
    EMPIRICAL SOFTWARE ENGINEERING, 2017, 22 (02) : 579 - 630
  • [34] Robust statistical methods in sodar studies of the ABL
    Simakhin, Valerii A.
    Cherepanov, Oleg S.
    Shamanaeva, Liudmila G.
    23RD INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS, 2017, 10466
  • [35] An Updated Guide to Robust Statistical Methods in Neuroscience
    Wilcox, Rand R.
    Rousselet, Guillaume A.
    CURRENT PROTOCOLS, 2023, 3 (03):
  • [36] A novel three-direction datum transformation of geodetic coordinates for Egypt using artificial neural network approach
    H. T. Elshambaky
    Mosbeh R. Kaloop
    Jong Wan Hu
    Arabian Journal of Geosciences, 2018, 11
  • [37] A novel three-direction datum transformation of geodetic coordinates for Egypt using artificial neural network approach
    Elshambaky, H. T.
    Kaloop, Mosbeh R.
    Hu, Jong Wan
    ARABIAN JOURNAL OF GEOSCIENCES, 2018, 11 (06)
  • [38] Improving Dose-Shaping and OAR-Sparing Using Robust Statistical Methods
    Sayre, G.
    Low, D.
    Ruan, D.
    MEDICAL PHYSICS, 2012, 39 (06) : 3847 - 3847
  • [39] THE MONITORING METHODOLOGY TUNNELS BASED INTEGRATED APPLICATIONS GEODETIC MEASUREMENT MEANS AND METHODS OF STATISTICAL
    Bogomolova, N. N.
    JOURNAL OF MINING INSTITUTE, 2013, 204 : 40 - 45
  • [40] Statistical tests of recent plate tectonic units by using geodetic data
    吕志平
    吴显兵
    张超
    Acta Seismologica Sinica(English Edition), 1997, (05) : 56 - 61