A hybrid method for accurate offset detection in GNSS time series

Abstract This study addresses the crucial detection of offsets in GNSS time series for accurate velocity estimations, vital in geosciences and engineering. Existing methods are challenged by correlated noise and can suffer from over-segmentation or high false positive rates. A novel methodology is proposed that combines a modified bilateral filter and an adapted implementation of the Pruned Exact Linear Time (PELT) algorithm, optimizing the detection of abrupt changes in the presence of complex noise. The technique was evaluated using a set of 3450 synthetic time series and 45 real time series from Global Navigation Satellite System (GNSS) stations located in Northern Europe. In the synthetic time series, the success threshold of 80 % was exceeded for the horizontal component with offsets of 1.2 mm, and the 90 % threshold for offsets of 1.4 mm. For the vertical component, 80 % success was achieved with offsets of 4 mm and 90 % with offsets of 5 mm. Regarding the real time-series, success percentages of 87.1 % were obtained for the East component, 74.4 % for the North component, and 74.2 % for the vertical component (Up). This hybrid technique enhances the identification of real offsets in GNSS time series, simplifying monitoring and paving the way for fully automated workflows.