Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Abstract

Global navigation satellite system (GNSS) technique has irreplaceable advantages in the continuous monitoring of surface deformation. Reducing noise to improve the signal-to-noise ratio (SNR) and extract the concerned signals is of great significance. As an improved algorithm of empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm has better signal processing ability. Using the CEEMDAN algorithm, the height time series of 29 GNSS stations in Chinese mainland were analyzed, and good denoising effects and extraction from periodic signals were achieved. The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis, and large differences in the long-term signals were found between the stations at different locations in Chinese mainland. With respect to data denoising, compared with the EMD and wavelet denoising algorithms, the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%, increased the correlation coefficient by 8.67% and 11.96%, and reduced root mean square error (RMSE) by 44.68% and 43.48%, indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms. In addition, the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity. The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm, which indicated that the CEEMDAN algorithm had advantages over frequency aliasing. In conclusion, the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.