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Long-term and seasonal displacements inferred from the regional GPS coordinate time series: case study in Central China Hefei City

  • Liansheng DengEmail author
  • Hua Chen
  • Jiafeng Ren
  • Yong Liao
Research Article

Abstract

Analyzing the GPS coordinate time series plays an important role in the crustal deformation monitoring and surface mass variation interpretations. In this paper, we present the results of detailed studies concerning the analysis of the long-term GPS coordinate time series. We show what the characteristics of all stations is monitored and how to keep track of the variations for both horizontal and vertical components by using Lomb-Scargle periodogram method and Maximum Likelihood Estimation method. For the Hefei area, the horizontal components exhibit obvious linear trend, while the vertical components display significant seasonal fluctuations which can be approximated by a function of an annual and a semi-annual signal. Under the optimal noise models, there is a consistent southeast trend, with an average of 35.21 mm/yr in the direction of E20.91°S, in the horizontal components under the ITRF2014. The relative velocities between different stations are with variations of 0.5~1.5 mm/yr, and the southern stations indicate bigger relative velocities than the northern stations. The mean vertical velocity is 1.31 mm/yr, revealing an overall uplift for this area. Moreover, the vertical displacements from the surface loading models (SLMs) are computed to interpret the seasonal GPS vertical crustal deformations. There are good consistencies between the GPS and SLMs data. The solutions of the two different types of data are closely to each other, and the mean correlation coefficient is 0.55 between the GPS and SLMs displacement time series, demonstrating that the seasonal variations might originate from the same geophysical process. Our analysis can provide useful reference for comprehensive analysis of the regional GPS coordinate time series.

Keywords

GPS time series Velocity Noise Seasonal signals Surface loading models 

Notes

Acknowledgements

We are grateful to the Surveying and Mapping Geomatics Center of Anhui and International GNSS Service (IGS) for providing the original datasets. Also many thanks for the editor and the anonymous reviewers for their constructive comments and suggestions, which help to improve the manuscript significantly. This research is supported by National Science Fund for Distinguished Young Scholars (No. 41525014), together with the Scientific Research Project of Hubei Provincial Department of Education (No. Q20194501), Surveying and Mapping Basic Research Program of National Administration of Surveying, Mapping and Geoinformation (No.17-01-01) and Research Program of Hubei Polytechnic University (No.18xjz09R).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Liansheng Deng
    • 1
    Email author
  • Hua Chen
    • 2
  • Jiafeng Ren
    • 3
  • Yong Liao
    • 4
  1. 1.Optics Valley BeiDou International SchoolHubei Polytechnic UniversityHuangshiChina
  2. 2.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  3. 3.Provincial Fundamental Geomatics Center of AnhuiHefeiChina
  4. 4.Huangshi Centre for Disease Control and PreventionHuangshiChina

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