Automated GNSS time series processing method and its application to coastal areas

Complex monitoring environments and deformation patterns can cause the Global Navigation Satellite System (GNSS) time series acquired by deformation monitoring to contain multi-source heterogeneous errors and signals, in order to meet the demand for high-precision deformation signal extraction from the GNSS time series, this paper investigates an automated processing method for GNSS time series based on individual corrections for outliers, step terms and error values. Firstly, this paper discuss and analyze the numerical change characteristics of various error terms, and then use the processing strategies of mean and standard deviation, data continuity discrimination and deformation characteristic function fitting to correct the outliers, step terms and error values in the GNSS time series, so as to improve the accuracy and reliability of the GNSS time series and obtain accurate deformation signals. In order to verify the validity of the method, the time series of 43 GNSS stations in Sumatra Island for the past 8 years were processed automatically, which showed that the estimated displacement rates showed similar characteristics and were consistent with the actual situation, with an eastward motion of 20-35 mm/year in the E direction, a northward motion of 15-30 mm/year in the N direction, and an uplift or subsidence of 10 mm/year in the U direction, thus verifying the validity of the method proposed in this paper for estimating displacement rates. In addition, the experimental results also indicated that the method in this paper can effectively realize the identification and removal of outliers, the repair of step terms, the correction of error values, the estimation of deformation characteristic parameters and the assessment of uncertainty, so as to provide accurate and reliable data for scientific research and engineering applications based on the GNSS time series, and it is suitable for the automated processing of the GNSS time series in the complex monitoring environment of the coastal area.