Seasonal variations of permanent stations in close vicinity to tectonic plate boundaries
-
Agata Bem
Abstract
With the increasing use and popularity of GNSS, there is a growing emphasis on understanding the characteristics of the signals and the impact on their outputs. This article provides an analysis of the daily solution of Iceland permanent GNSS stations daily time series in near proximity to tectonic plate boundaries, aiming to investigate seasonal changes in coordinate values. As a part of the study, the data are prepared, and a function is fitted using the method of least squares, providing for further analysis coefficients and the quality of fit. The research reveals no unequivocal correlation between location and the height of annual amplitudes, except for the stations situated on the Vatnajökull ice cap. It consistently demonstrates higher seasonal changes compared to others, which indicates the influence of snow and water load. Excluding these results, the annual displacement for horizontal components is approximately 1 mm, while the average yearly amplitude for vertical components is almost 5 mm. The results concerned with the Up generally exhibit larger values compared to the other components. It is observed that the mean station variations are higher for the Eurasian plate. The quality of the fit, with regard to outliers and RMSE, does not demonstrate a correlation between the duration of the time series. Furthermore, the average percentage of detected outlier observations is higher for the North American plate.
Acknowledgments
The author would like to extend gratitude to Kamil Maciuk of AGH University of Krakow for his substantial contributions to the improvement of this manuscript. The author is grateful for the insightful comments and suggestions offered by the anonymous reviewers and the editors.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Tunini, L, Zuliani, D, Magrin, A. Applicability of cost-effective GNSS sensors for crustal deformation studies. Sensors 2022;22:350. https://doi.org/10.3390/s22010350.Search in Google Scholar PubMed PubMed Central
2. Peng, D, Hill, EM, Li, L, Switzer, AD, Larson, KM. Application of GNSS interferometric reflectometry for detecting storm surges. GPS Solut 2019;23:1–11. https://doi.org/10.1007/s10291-019-0838-y.Search in Google Scholar
3. Van Dam, T, Wahr, J, Milly, PCD, Shmakin, AB, Blewitt, G, Lavallée, D, et al.. Crustal displacements due to continental water loading. Geophys Res Lett 2001;28:651–4. https://doi.org/10.1029/2000GL012120.Search in Google Scholar
4. VanDam, TM, Wahr, J, Chao, Y, Leuliette, E. Predictions of crustal deformation and of geoid and sea-level variability caused by oceanic and atmospheric loading. Geophys J Int 1997;129:507–17. https://doi.org/10.1111/j.1365-246x.1997.tb04490.x.Search in Google Scholar
5. Maciuk, K, Szombara, S. Annual crustal deformation based on GNSS observations between 1996 and 2016. Arabian J Geosci 2018;11:667. https://doi.org/10.1007/s12517-018-4022-4.Search in Google Scholar
6. Bogusz, J, Figurski, M, Kroszczyński, K, Szafranek, K. Investigation of environmental influences to the precise GNNS solutions. Acta Geodyn Geomater 2011;8:5–15.Search in Google Scholar
7. Ansari, K, Park, KD, Kubo, N. Linear time-series modeling of the GNSS based TEC variations over Southwest Japan during 2011–2018 and comparison against ARMA and GIM models. Acta Astronaut 2019;165:248–58. https://doi.org/10.1016/j.actaastro.2019.09.017.Search in Google Scholar
8. Davis, JL, Wernicke, BP, Tamisiea, ME. On seasonal signals in geodetic time series. J Geophys Res Solid Earth 2012;117:1–10. https://doi.org/10.1029/2011JB008690.Search in Google Scholar
9. Mao, A, Harrison, GA, Dixon, H. Noise in GPS coordinate time series. J Geophys Res Solid Earth 1999;104:2797–816. https://doi.org/10.1029/1998jb900033.Search in Google Scholar
10. Santamaría-Gómez, A, Bouin, M-N, Collilieux, X, Wöppelmann, G. Correlated errors in GPS position time series: implications for velocity estimates. J Geophys Res Solid Earth 2011;116. https://doi.org/10.1029/2010JB007701.Search in Google Scholar
11. Klos, A, Bogusz, J, Figurski, M, Gruszczynska, M, Gruszczynski, M. Investigation of noises in the EPN weekly time series. Acta Geodyn Geomater 2015;12:117–26. https://doi.org/10.13168/AGG.2015.0010.Search in Google Scholar
12. Amiri-Simkooei, AR. Non-negative least-squares variance component estimation with application to GPS time series. J Geodesy 2016;90:451–66. https://doi.org/10.1007/s00190-016-0886-9.Search in Google Scholar
13. He, X, Bos, MS, Montillet, JP, Fernandes, RMS. Investigation of the noise properties at low frequencies in long GNSS time series. J Geodesy 2019;93:1271–82. https://doi.org/10.1007/s00190-019-01244-y.Search in Google Scholar
14. He, Y, Nie, G, Wu, S, Li, H. Analysis and discussion on the optimal noise model of global GNSS long-term coordinate series considering hydrological loading. Rem Sens 2021;13:431. https://doi.org/10.3390/rs13030431.Search in Google Scholar
15. Blewitt, G, Lavallée, D. Effect of annual signals on geodetic velocity. J Geophys Res Solid Earth 2002;107. https://doi.org/10.1029/2001jb000570.Search in Google Scholar
16. Bos, MS, Bastos, L, Fernandes, RMS. The influence of seasonal signals on the estimation of the tectonic motion in short continuous GPS time-series. J Geodyn 2010;49:205–9. https://doi.org/10.1016/j.jog.2009.10.005.Search in Google Scholar
17. Klos, A, Bos, MS, Bogusz, J. Detecting time-varying seasonal signal in GPS position time series with different noise levels. GPS Solut 2018;22:1–11. https://doi.org/10.1007/s10291-017-0686-6.Search in Google Scholar
18. Savchuk, S, Doskich, S, Gołda, P, Rurak, A. The seasonal variations analysis of permanent GNSS station time series in the central-east of Europe. Rem Sens 2023;15:1–18. https://doi.org/10.3390/rs15153858.Search in Google Scholar
19. Maciuk, K, Varna, I, Xu, C. Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions. J Appl Geodesy 2021;15:61–73. https://doi.org/10.1515/jag-2020-0042.Search in Google Scholar
20. Bogusz, J, Figurski, M. Annual signals observed in regional GPS networks. Acta Geodyn Geomater 2014;11:125–31. https://doi.org/10.13168/AGG.2014.0003.Search in Google Scholar
21. Maciuk, K. The study of seasonal changes of permanent stations coordinates based on weekly EPN solutions. Artif Satell 2016;51:1–18. https://doi.org/10.1515/arsa-2016-0001.Search in Google Scholar
22. Altamimi, Z, Métivier, L, Rebischung, P, Collilieux, X, Chanard, K, Barnéoud, J. ITRF2020 plate motion model. Geophys Res Lett 2023;50:1–7. https://doi.org/10.1029/2023GL106373.Search in Google Scholar
23. Nistor, S, Suba, NS, El-Mowafy, A, Apollo, M, Malkin, Z, Nastase, EI, et al.. Implication between geophysical events and the variation of seasonal signal determined in GNSS position time series. Rem Sens 2021;13:3478. https://doi.org/10.3390/rs13173478.Search in Google Scholar
24. Sigmundsson, F, Hreinsdóttir, S, Hooper, A, Árnadóttir, T, Pedersen, R, Roberts, MJ, et al.. Intrusion triggering of the 2010 Eyjafjallajökull explosive eruption. Nature 2010;468:426–32. https://doi.org/10.1038/nature09558.Search in Google Scholar PubMed
25. Pırtı, A, Hoşbaş, RG, Yücel, MA. Monitoring of Icelandic plate movement with GNSS method and GPS signal jamming effects in Iceland. Rep Geodesy Geoinf 2023;116:15–22. https://doi.org/10.2478/rgg-2023-0007.Search in Google Scholar
26. Gudmundsson, A. Infrastructure and mechanics of volcanic systems in Iceland. J Volcanol Geoth Res 1995;64:1–22. https://doi.org/10.1016/0377-0273(95)92782-Q.Search in Google Scholar
27. Grapenthin, R, Sigmundsson, F, Geirsson, H, Árnadóttir, T, Pinel, V. Icelandic rhythmics: annual modulation of land elevation and plate spreading by snow load. Geophys Res Lett 2006;33:1–5. https://doi.org/10.1029/2006GL028081.Search in Google Scholar
28. Drouin, V, Heki, K, Sigmundsson, F, Hreinsdóttir, S, Ófeigsson, BG. Constraints on seasonal load variations and regional rigidity from continuous GPS measurements in Iceland, 1997–2014. Geophys J Int 2016;205:1843–58. https://doi.org/10.1093/gji/ggw122.Search in Google Scholar
29. Compton, K, Bennett1, RA, Hreinsdottir, S, Bordoni, A, Barletta, V, Spada, G. Short-term variations of Icelandic ice cap mass inferred from cGPS coordinate time series. Geochem Geophys Geosyst 2017;18:2099–2119. https://doi.org/10.1002/2017GC006831.Search in Google Scholar
30. Amiri-Simkooei, AR, Tiberius, CCJM, Teunissen, PJG. Assessment of noise in GPS coordinate time series: methodology and results. J Geophys Res Solid Earth 2007;112:1–19. https://doi.org/10.1029/2006JB004913.Search in Google Scholar
31. Chen, B, Bian, J, Ding, K, Wu, H, Li, H. Extracting seasonal signals in GNSS coordinate time series viaw eighted nuclear norm minimization. Rem Sens 2020;12:11–18. https://doi.org/10.3390/rs12122027.Search in Google Scholar
32. Bennett, RA. Instantaneous deformation from continuous GPS: contributions from quasi-periodic loads. Geophys J Int 2008;174:1052–64. https://doi.org/10.1111/j.1365-246X.2008.03846.x.Search in Google Scholar
33. Yao, Y. On the non-linear motion of IGS station. Geo-Spatial Inf Sci 2007;10:240–4. https://doi.org/10.1007/s11806-007-0101-5.Search in Google Scholar
34. Li, W, Shen, Y. Velocity estimation of GPS base stations considering the coloured noises. J Appl Geodesy 2012;6:149–57. https://doi.org/10.1515/jag-2012-0014.Search in Google Scholar
35. Geirsson, H, Lafemina, P, Rnadóttir, T, Sturkell, E, Sigmundsson, F, Travis, M, et al.. Volcano deformation at active plate boundaries: deep magma accumulation at Hekla volcano and plate boundary deformation in south Iceland. J Geophys Res Solid Earth 2012;117:1–18. https://doi.org/10.1029/2012JB009400.Search in Google Scholar
36. Blewitt, G, Hammond, W, Kreemer, C. Harnessing the GPS data explosion for interdisciplinary science. Eos 2018;99. https://doi.org/10.1029/2018eo104623.Search in Google Scholar
37. Bogusz, J, Klos, A. On the significance of periodic signals in noise analysis of GPS station coordinates time series. GPS Solut 2016;20:655–64. https://doi.org/10.1007/s10291-015-0478-9.Search in Google Scholar
38. Leys, C, Ley, C, Klein, O, Bernard, P, Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 2013;49:764–6. https://doi.org/10.1016/j.jesp.2013.03.013.Search in Google Scholar
39. MathWorks. MATLAB function reference. In: MATLAB man; 2024.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Special Issue: Joint International Symposium on Deformation Monitoring 2025
- Impact of mathematical correlations
- Employing variance component estimation for point cloud based geometric surface representation by B-splines
- Deterministic uncertainty for terrestrial laser scanning observations based on intervals
- Investigating the potential of stochastic relationships to model deformations
- Laser scanning based deformation analysis of a wooden dome under load
- Classifying surface displacements in mining regions using differential terrain models and InSAR coherence
- Water multipath effect in Terrestrial Radar Interferometry (TRI) in open-pit mine monitoring
- Multi-temporal GNSS, RTS, and InSAR for very slow-moving landslide displacement analysis
- Reviews
- Evaluation of the regional ionosphere using final, ultra-rapid, and rapid ionosphere products
- Experiences with techniques and sensors for smartphone positioning
- Original Research Articles
- Crustal deformation estimation using InSAR, West of the Gulf of Suez, Egypt
- Factors affecting the fitting of a global geopotential model to local geodetic datasets over local areas in Egypt using multiple linear regression approach
- Utilization of low-cost GNSS RTK receiver for accurate GIS mapping in urban environment
- Seasonal variations of permanent stations in close vicinity to tectonic plate boundaries
- Time-frequency and power-law noise analyzes of three GBAS solutions of a single GNSS station
- A 2D velocity field computation using multi-dimensional InSAR: a case study of the Abu-Dabbab area in Egypt
Articles in the same Issue
- Frontmatter
- Special Issue: Joint International Symposium on Deformation Monitoring 2025
- Impact of mathematical correlations
- Employing variance component estimation for point cloud based geometric surface representation by B-splines
- Deterministic uncertainty for terrestrial laser scanning observations based on intervals
- Investigating the potential of stochastic relationships to model deformations
- Laser scanning based deformation analysis of a wooden dome under load
- Classifying surface displacements in mining regions using differential terrain models and InSAR coherence
- Water multipath effect in Terrestrial Radar Interferometry (TRI) in open-pit mine monitoring
- Multi-temporal GNSS, RTS, and InSAR for very slow-moving landslide displacement analysis
- Reviews
- Evaluation of the regional ionosphere using final, ultra-rapid, and rapid ionosphere products
- Experiences with techniques and sensors for smartphone positioning
- Original Research Articles
- Crustal deformation estimation using InSAR, West of the Gulf of Suez, Egypt
- Factors affecting the fitting of a global geopotential model to local geodetic datasets over local areas in Egypt using multiple linear regression approach
- Utilization of low-cost GNSS RTK receiver for accurate GIS mapping in urban environment
- Seasonal variations of permanent stations in close vicinity to tectonic plate boundaries
- Time-frequency and power-law noise analyzes of three GBAS solutions of a single GNSS station
- A 2D velocity field computation using multi-dimensional InSAR: a case study of the Abu-Dabbab area in Egypt
