Analysis of seasonal signals and long-term trends in the height time series of IGS sites in China

Abstract

The seasonal signal and long-term trend in the height time series of 10 IGS sites in China are investigated in this paper. The offset detection and outlier removal as well as the removal of common mode error are performed on the raw GPS time-series data developed by the Scripps Orbit and Permanent Array Center (SOPAC). The seasonal-trend decomposition procedure based on LOESS (STL) is utilized to extract precise seasonal signals, followed by an estimation of the long-term trend with the application of maximum likelihood estimation (MLE) to the seasonally adjusted time series. The Up-components of all sites are featured by obvious seasonal variations, with significant phase and amplitude modulation on some sites. After Kendall’s tau test, a significant trend (99% confidence interval) for all sites is achieved. Furthermore, the trends at sites TCMS and TNML have significant changes at epochs 2009.5384 and 2009.1493 (95% confidence interval), respectively, using the Breaks For Additive Seasonal and Trend test. Finally, the velocities and their uncertainties for all sites are estimated using MLE with the white noise plus flicker noise model. And the results are analyzed and compared with those announced by SOPAC. The results obtained in this paper have a higher precision than the SOPAC results.