Detection of Periodic Signals With Time-Varying Coefficients From CMONOC Stations in China by Singular Spectrum Analysis

GNSS coordinate time series reflects the combined influence of geophysical factors on stations around the land surface. Although some traditional parameterized methods are helpful to determine the magnitude of the seasonal signal at GNSS stations, the annual variation characteristics of the stations are not static, thus it is quite necessary to extract finer periodic signals with time-varying coefficients (PSTC) from stations’ position time series. This paper focuses on the height time series of 243 stations from the Crustal Movement Observation Network of China (CMONOC) and employs singular spectrum analysis (SSA) to extract PSTC. The results show that SSA method can effectively extract the time-varying trend and periodic terms from the original time series, which cannot be perfectly achieved by parameterized methods. SSA method reduces the RMSE value of the residual time series at 90.5% CMONOC stations, compared with the results of maximum likelihood estimation (MLE). Its function in extracting the PSTC from CMONOC stations is significant for further explaining the generation mechanism of the land surface nonlinear deformation in China. Different from MLE method which only considers the given epochs of offsets, SSA method can effectively fit the original time series through singular value decomposition (SVD) and signal reconstruction, despite there are unrecognized offsets contained in GNSS time series. It still works well when there is an offset up to 20 mm, which would reduce the traditional workload of offset detection by sight. SSA method manages to distinguish large unknown offsets, showing as negative improvement rates.