Abstract
Traditional automated offset detections on global navigation satellite system (GNSS) station coordinate time series still cannot fully replace manual detections in practical applications due to their high false positive detection rates. We developed preliminary and enhanced offset detection approaches and tested them against the solutions from the International GNSS service 2nd and 3rd data reprocessing campaigns (Repro2 and Repro3). Their manually detected offset recordings in International Terrestrial Reference Frame (ITRF) 2014 and ITRF2020 are used as evaluation criteria. In the preliminary approaches, stochastic models based on covariance matrix, white noise model, and white noise plus flicker noise model of both univariate and multivariate are studied. Although we achieved true positive, false positive, and false negative (TP, FP, FN) rates of (0.44, 0.40, 0.16) for Repro2 and (0.42, 0.44, 0.13) for Repro3, the preliminary automated detections still lead to many false positive detections. Thus, based on the preliminary approaches, and ancillary data, an enhanced detection approach is proposed. Enhanced detections significantly reduce 56% ~ 80% false positive detections compared to preliminary approaches. As a result, for Repro3, the optimal overall performance is attained with (TP, FP, FN) rates of (0.57, 0.25, 0.18), along with a detection rate of 75%; for Repro2, the rates are (0.58, 0.20, 0.22), accompanied by a 73% detection rate. The current enhanced approach may serve as a supplementary or reference to manual detection, although still not being perfect. Furthermore, 20 manually detected unknown offsets in ITRF2020 are found to correspond to some known events (13 earthquakes and 7 equipment changes); 34 automated detections that correspond to known events but are not collected in ITRF2020 are manually checked as offsets (14 earthquakes and 20 equipment changes).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The IGS repro2 and repro3 time series are available from the https://cddis.nasa.gov/archive/gnss/products/. The NGL earthquake catalogue data are obtained from http://geodesy.unr.edu/NGLStationPages/. The site log files are available from ftp://garner.ucsd.edu/pub/docs/station_logs/, ftp://ftp.geonet.org.nz/gps/sitelogs/logs/and ftp://igs.org/pub/station/log/.
References
Agnew DC (1992) The time-domain behavior of power-law noises. Geophys Res Lett 19:333–336. https://doi.org/10.1029/91gl02832
Altamimi Z, Rebischung P, Métivier L et al (2016) ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions. J Geophys Res: Solid Earth 121:6109–6131. https://doi.org/10.1002/2016jb013098
Altamimi Z, Rebischung P, Collilieux X et al (2023) ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions. J Geodes 97:47. https://doi.org/10.1007/s00190-023-01738-w
Amiri-Simkooei AR (2008) Noise in multivariate GPS position time-series. J Geodesy 83:175–187. https://doi.org/10.1007/s00190-008-0251-8
Amiri-Simkooei AR, Tiberius CCJM, Teunissen PJG (2007) Assessment of noise in GPS coordinate time series: Methodology and results. J Geophys Res: Solid Earth 112:B7. https://doi.org/10.1029/2006jb004913
Amiri-Simkooei AR, Mohammadloo TH, Argus DF (2017) Multivariate analysis of GPS position time series of JPL second reprocessing campaign. J Geodesy 91:685–704. https://doi.org/10.1007/s00190-016-0991-9
Amiri-Simkooei AR, Hosseini-Asl M, Asgari J et al (2018) Offset detection in GPS position time series using multivariate analysis. GPS Solut 23:12. https://doi.org/10.1007/s10291-018-0805-z
Blewitt G, Hammond W, Kreemer C (2018) Harnessing the GPS data explosion for interdisciplinary science. Eos. https://doi.org/10.1029/2018eo104623
Bock Y, Melgar D (2016) Physical applications of GPS geodesy: a review. Rep Prog Phys 79:106801. https://doi.org/10.1088/0034-4885/79/10/106801
Bos MS, Fernandes RMS, Williams SDP et al (2013) Fast error analysis of continuous GNSS observations with missing data. J Geodesy 87:351–360. https://doi.org/10.1007/s00190-012-0605-0
Bruni S, Zerbini S, Raicich F et al (2014) Detecting discontinuities in GNSS coordinate time series with STARS: case study, the Bologna and Medicina GPS sites. J Geodesy 88:1203–1214. https://doi.org/10.1007/s00190-014-0754-4
Gazeaux J, Williams S, King M et al (2013) Detecting offsets in GPS time series: first results from the detection of offsets in GPS experiment. J Geophys Res: Solid Earth 118:2397–2407. https://doi.org/10.1002/jgrb.50152
Griffiths J, Ray J (2015) Impacts of GNSS position offsets on global frame stability. Geophys J Int 204:480–487. https://doi.org/10.1093/gji/ggv455
Herring TA, Melbourne TI, Murray MH et al (2016) Plate boundary observatory and related networks: GPS data analysis methods and geodetic products. Rev Geophys 54:759–808. https://doi.org/10.1002/2016rg000529
Khazraei SM, Amiri-Simkooei AR (2021) Improving offset detection algorithm of GNSS position time-series using spline function theory. Geophys J Int 224:257–270. https://doi.org/10.1093/gji/ggaa453
Lahtinen S, Jivall L, Häkli P et al (2021) Updated GNSS velocity solution in the Nordic and Baltic countries with a semi-automatic offset detection method. GPS Solut 26:12. https://doi.org/10.1007/s10291-021-01194-z
Lian L, Wang J, Huang C et al (2018) Weekly inter-technique combination of SLR, VLBI, GPS and DORIS at the solution level. Res Astron Astrophys 18:119. https://doi.org/10.1088/1674-4527/18/10/119
Mao A, Harrison CGA, Dixon TH (1999) Noise in GPS coordinate time series. J Geophys Res: Solid Earth 104:2797–2816. https://doi.org/10.1029/1998jb900033
Najder J (2020) Automatic detection of discontinuities in the station position time series of the reprocessed global GNSS network using bernese GNSS software. Acta Geodynamica Et Geomaterialia 17:439–451. https://doi.org/10.13168/Agg.2020.0032
Ostini L, Dach R, Schaer S, et al. (2008). FODITS: A new tool of the Bernese GPS software to analyze time series. In: EUREF Symposium 2008, Brussels, Belgium. https://www.researchgate.net/publication/237596296
Perfetti N (2006) Detection of station coordinate discontinuities within the Italian GPS Fiducial Network. J Geodesy 80:381–396. https://doi.org/10.1007/s00190-006-0080-6
Teunissen PJG (1985) Quality control in geodetic networks. In: Grafarend EW, Sansò F (eds) Optimization and Design of Geodetic Networks. Springer, Berlin, pp 526–547. https://doi.org/10.1007/978-3-642-70659-2_18
Teunissen PJG (1990) Quality control in integrated navigation systems. IEEE Aerosp Electron Syst Mag 5:35–41. https://doi.org/10.1109/62.134219
Teunissen PJG (1998) Quality control and GPS. In: Teunissen PJG, Kleusberg A (eds) GPS for Geodesy. Springer, Berlin, pp 271–318
Teunissen PJG (2017) Distributional theory for the DIA method. J Geodesy 92:59–80. https://doi.org/10.1007/s00190-017-1045-7
Vitti A (2012) Sigseg: a tool for the detection of position and velocity discontinuities in geodetic time-series. GPS Solut 16:405–410. https://doi.org/10.1007/s10291-012-0257-9
Wang L, Herring T (2019) Impact of estimating position offsets on the uncertainties of GNSS site velocity estimates. J Geophys Res: Solid Earth 124:13452–13467. https://doi.org/10.1029/2019jb017705
Williams SDP (2003a) The effect of coloured noise on the uncertainties of rates estimated from geodetic time series. J Geodesy 76:483–494. https://doi.org/10.1007/s00190-002-0283-4
Williams SDP (2003b) Offsets in Global Positioning System time series. J Geophys Res: Solid Earth 108:13. https://doi.org/10.1029/2002jb002156
Williams SDP, Bock Y, Fang P et al (2004) Error analysis of continuous GPS position time series. J Geophys Res: Solid Earth 109:412. https://doi.org/10.1029/2003jb002741
Wu D, Yan H, Yuan S (2018) L1 regularization for detecting offsets and trend change points in GNSS time series. GPS Solut 22:5. https://doi.org/10.1007/s10291-018-0756-4
Yang L, Shen Y, Li B et al (2021) Simplified algebraic estimation for the quality control of DIA estimator. J Geodesy 95:15. https://doi.org/10.1007/s00190-020-01454-9
Zaminpardaz S, Teunissen PJG (2019) DIA-datasnooping and identifiability. J Geodesy 93:85–101. https://doi.org/10.1007/s00190-018-1141-3
Zhang J, Bock Y, Johnson H et al (1997) Southern California permanent GPS geodetic array: error analysis of daily position estimates and site velocities. J Geophys Res: Solid Earth 102:18035–18055. https://doi.org/10.1029/97jb01380
Acknowledgements
This Study is supported by National Nature Science Foundation of China (Grants 12233010, 11903065).
Author information
Authors and Affiliations
Contributions
Jin Zhang did conceptualization, methodology, software, writing original draft. Lizhen Lian and Chengli Huang designed this research, did review and editing. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, J., Lian, L., Huang, C. et al. Automated offset detection approaches: case study in IGS Repro2 and 3. GPS Solut 28, 123 (2024). https://doi.org/10.1007/s10291-024-01662-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-024-01662-2