Determination of the correlation degree between GNSS stations of Ukraine based on time series

Keywords: GNSS data processing, GNSS time series, PCA, CME, noise analysis


Using GNSS for many years is the most common technology for the collection, processing, and interpretation of Earth observation data, in particular for the high-precision study of plate tectonics. The results of GNSS observations, such as coordinate time series, allow us to do continuous monitoring of stations, and modern methods of satellite observation processing provide high-precision results for geodynamic interpretation. The aim of our study is to process the results of observations by DD and PPP methods and determine the degree of correlation between GNSS stations based on coordinate time series. For our study, we selected 10 GNSS stations, which merged into two networks - Lviv (SAMB, STOY, STRY, SULP та ZLRS) and Ukrainian (BCRV, CHTK, CNIV, CRNI, GLSV та SULP). The duration of observations on each of them is about 1.5 years (2019-2020). The downloaded observation files were processed in two software packages: Gamit and GipsyX. After applying the «cleaned» procedures based on the iGPS software package, the residual time series were obtained and the coefficients of the interstation correlation matrices were calculated. After the procedure of "cleaning" the time series, we obtained the RMS value decrease for all components of the coordinates by an average of 7-30%, and some stations by 55%. Based on the obtained RMS values, we can conclude that the influence of unextracted or incorrectly modeled errors can significantly affect the results of satellite observations. The obtained interstation correlation coefficients for both networks show different results depending on the used method for processing satellite observations. The larger correlation values of the DD method can be explained by the fact that the effect of errors is distributed evenly to all network stations, whereas in the PPP method errors for each station are individual. The obtained graphs of the common-mode errors values, after their removal from the residual time series, confirm the more uniform nature of the DD method. The results of our study indicate the feasibility of using the PPP method, as the autonomous processing of stations allows you to see the real geodynamic picture of the studied region.


Bogusz, J., Gruszczynski, M., Figurski, M., Klos, A. (2015). Spatio-temporal filtering for determination of common mode error in regional GNSS networks. Open Geosciences, 1(open-issue), 140-148. doi: 0.1515/geo-2015-0021

Dong, D., Fang, P., Bock, Y., Cheng, M. K., Miyazaki, S. I. (2002). Anatomy of apparent seasonal variations from GPS‐derived site position time series. Journal of Geophysical Research: Solid Earth, 107(B4), ETG-9, 9-16. doi:10.1029/2001JB000573

Dong, D., Fang, P., Bock, Y., Webb, F., Prawirodirdjo, L., Kedar, S., Jamason, P. (2006). Spatiotemporal filtering using principal component analysis and Karhunen‐Loeve expansion approaches for regional GPS network analysis. Journal of Geophysical Research: Solid Earth, 111(B3), 1-16. doi:10.1029/2005JB003806

Fu, Y., Freymueller, J. T. (2012). Seasonal and long‐term vertical deformation in the Nepal Himalaya constrained by GPS and GRACE measurements. Journal of Geophysical Research: Solid Earth, 117(B3), 1-14. doi:10.1029/2011JB008925

GipsyXDocs, 2019

Herring, T. A., King, R. W., McClusky, S. C. (2010). Introduction to gamit/globk, 1-36, Massachusetts Institute of Technology, Cambridge, Massachusetts

Hofmann-Wellenhof, B., Lichtenegger, H., Wasle, E. (2007). GNSS–global navigation satellite systems: GPS, GLONASS, Galileo, and more, 169-172, Springer Science & Business Media

Ji, K., Herring, T. (2011). Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008. Geophysical Research Letters, 38(6), 1-5. doi:10.1029/2011GL046904

Nikolaidis, R. (2004). Observation of geodetic and seismic deformation with the Global Positioning System, Ph.D. thesis, Univ. of Calif., San Diego

Savchuk, S., Khoptar, A., Sosonka, I. (2020). Processing of a regional network of GNSS stations by the PPP method. Wybrane aspekty zabezpieczenia nawigacji lotniczej, Seria wydawnicza “Problemy współczesnej nawigacji” , Część 2, 159-170

Tian, Y. (2011). iGPS: IDL tool package for GPS position time series analysis. GPS Solutions, 15(3), 299-303. doi:10.1007/s10291-011-0219-7

Tian, Y., Shen, Z. (2011). Correlation weighted stacking filtering of common-mode component in GPS observation network. Acta Seismol. Sin, 33(2), 198-208.

Zumberge, J. F., Heflin, M. B., Jefferson, D. C., Watkins, M. M., Webb, F. H. (1997). Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of Geophysical Research: Solid Earth, 102(B3), 5005-5017. doi:10.1029/96JB03860

Earth observation data applications: Challenges and tasks