Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. If these are not detected and adjusted correctly they bias velocities, and hence geophysical estimates, and degrade the terrestrial reference frame. They also alter apparent time series noise characteristics as undetected offsets resemble a random walk process. As such, offsets are a substantial problem. A number of offset detection methods have been developed across a range of fields, and some of these are now being tested in geodetic time series.
The DOGEx (Detection of Offsets in GPS Experiment) project aims to test the effectiveness of automated and manual offset detection approaches and the subsequent effect on GPS-derived velocities. To do this, simulated time series were first generated that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. We focus on offset detection and together with velocity biases induced by incorrect offset detection. We show that, at present, manual methods (where offsets are hand -picked by GPS time series experts) almost always give better results than automated or semi-automated methods (two automated methods give quite similar velocity bias as the best
manual solutions). For instance, the 5th percentile ranges (5% to 95%) in velocity bias for automated approaches is equal to 4.2mm/year,whereas it is equal to 1.8mm/yr for the manual solutions. However the True Positive detection rate of automated solutions is significantly higher than those for the manual solutions, being around 37% for the best automated, and 42% for the best manual solution. The amplitude of offsets detectable by automated solutions is greater than for hand picked solutions, with the smallest detectable offset for the two best manual solutions equal to 5mm and 7mm and to 8mm and 10mm for the two best automated solutions. The best manual solutions yielded velocity biases from the truth commonly in the range ±0.2mm/yr,
whereas the best automated solutions produced biases no better than double this range. Assuming the simulated time series noise levels continue to be representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than these levels is
therefore not robust. Further work is required before we can routinely interpret sub-mm/yr velocities for single GPS stations.