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

By J. Gazeaux, Simon D. P. Williams, Matt A. King, Machiel Simon Bos, Rolf Dach, M. Deo, Angelyn W. Moore, L. Ostini, E. Petrie, M. Roggero and 3 more. 2013

188 citations recorded by the source corpus.

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. To assess the effectiveness of methods that detect and remove these offsets, we designed and managed the Detection of Offsets in GPS Experiment. We simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. The data set was made available to the GPS analysis community without revealing the offsets, and several groups conducted blind tests with a range of detection approaches. The results show that, at present, manual methods (where offsets are hand picked) 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 fifth percentile range (5% to 95%) in velocity bias for automated approaches is equal to 4.2 mm/year (most commonly ±0.4 mm/yr from the truth), whereas it is equal to 1.8 mm/yr for the manual solutions (most commonly 0.2 mm/yr from the truth). The magnitude of offsets detectable by manual solutions is smaller than for automated solutions, with the smallest detectable offset for the best manual and automatic solutions equal to 5 mm and 8 mm, respectively. Assuming the simulated time series noise levels are representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than 0.2–0.4 mm/yr is therefore certainly not robust, although a limit of nearer 1 mm/yr would be a more conservative choice. Further work to improve offset detection in GPS coordinates time series is required before we can routinely interpret sub‒mm/yr velocities for single GPS stations.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/635ef88ba4fa130089d87f447e7b2167d6aaa17c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Ksenia Dmitrieva
2. Paul Segall
3. C. Demets
4. Alireza Amiri-Simkooei
5. Yuanxi Yang

In Artificial Intelligence

1. C. L'evy-Leduc
2. Alireza Amiri-Simkooei
3. Stéphane Robin
4. Bin Zhao
5. Feng Ming
6. Anmin Zeng
7. Yuanxi Yang

In Computer Vision and Pattern Recognition

1. Alireza Amiri-Simkooei
2. X. Fang

Citations in the corpus

• Network-based estimation of time-dependent noise in GPS position time series (2015, methodology): “While the larger antenna offsets were removed from the data, it has been shown that there is no foolproof method for removing small offsets (Gazeaux et al. 2013), and these could certainly bias station velocities.”
• A robust approach for estimating change-points in the mean of an AR(1) process (2014, background): “Change-point detection problems arise in many fields, such as genomics [8,9,31], medical imaging [20], earth sciences [15,36], econometrics [17,19] or climate [25,28].”
• Non-negative least-squares variance component estimation with application to GPS time series (2016, methodology): “Possible offsets of the series have also been identified and corrected prior to the analysis (Williams 2003b; Gazeaux et al. 2013).”
• Spatiotemporal filtering for regional GPS network in China using independent component analysis (2017, methodology): “To improve the accuracy of detection of the offsets, other unknown-type offsets were identified through visual inspection (Gazeaux et al. 2013;Ming et al. 2016).”