Method for detecting short-term displacements of the Earth's surface by statistical analysis of GNSS time series

Department of Higher Geodesy and Astronomy of Lviv Polytechnic National University
Department of Higher Geodesy and Astronomy of Lviv Polytechnic National University

Short-term geodynamic displacements of the Earth's surface are studied insufficiently because the unambiguous identification of such geodynamic processes is quite a difficult task. Short-term geodynamic processes can be observed by considering GNSS time series lasting up to 2 months. The coordinate displacements are visually almost unnoticeable comparing annual time series. In this work, an algorithm based on the results of statistical analysis of time series of several GNSS stations on purpose to find simultaneous displacements of the Earth's surface is developed. Authors propose a method for detecting short-term displacements based on sliding correlation and covariance interrelationships between the time series of two GNSS stations for short periods, which are shifted along with the entire time series. The approach allows showing the characteristic of the displacements throughout the study area based on the selection of anomalous displacements of selected GNSS stations. The high correlation coefficient between the periods of stations indicates the presence of simultaneous and identical in absolute value offsets. The high value of covariance indicates the synchronicity and unidirectionality of such displacements. As a result, the time series of 8 GNSS stations of the Geoterrace network for the period from the end of 2017 to the beginning of 2021 are studied according to the presented method. The anomalous altitude displacements in the region for the epoch of 185th day of 2018 and 20 days period is investigated. Based on the processing, maps of the spatial distribution of correlation and covariance coefficients are constructed. The proposed method could be improved and applied to the study of kinematic processes in areas with a dense network of GNSS stations with long time series similarly GNSS networks for monitoring of large electricity produced objects such as HPPs and PSPs.

1. Blewitt, G., Kreemer, C., Hammond, W. C., & Gazeaux, J. (2016). MIDAS robust trend estimator for accurate GPS station velocities without step detection. Journal of Geophysical Research: Solid Earth, 121(3), 2054-2068.
2. Bos, M., Fernandes, R., & Bastos, L. (2019). Hector user manual version 1.7. 2.
3. Brusak, I., & Tretyak, K. (2020, December). About the phenomenon of subsidence in continental Europe in December 2019 based on the GNSS stations data. In International Conference of Young Professionals «GeoTerrace-2020» (No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
4. Dach, R., Lutz, S., Walser, P., & Fridez, P. (2015). Bernese GNSS software version 5.2.
5. Geoterrace. Network of active permanent GNSS stations. (in Ukrainian).
6. He, X., Yu, K., Montillet, J. P., Xiong, C., Lu, T., Zhou, S., ... & Ming, F. (2020). GNSS-TS-NRS: An Open-Source MATLAB-Based GNSS Time Series Noise Reduction Software. Remote Sensing, 12(21), 3532.
7. Montillet, J. P., & Bos, M. S. (Eds.). (2019). Geodetic Time Series Analysis in Earth Sciences. Springer.
8. Santamaría-Gómez, A. (2019). SARI: interactive GNSS position time series analysis software. GPS solutions, 23(2), 1-6.
9. Savchyn, I., Tretyak, K., Petrov, S., Zaiats, O., & Brusak, I. (2019, June). Monitoring of mine fields at Stebnyk po¬tassium deposit area by a geodetic and geotechnical me¬thod. In First EAGE Workshop on Assessment of Landslide and Debris Flows Hazards in the Carpathians (No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
10. Tian Y. (2011). iGPS: IDL tool package for GPS position time series analysis. GPS Solution, 15(3), 299-303
11. Tretyak, K., Cranenbroeck, J., Balan A., Lompas, O., & Savchyn, I. (2014). A posteriori optimization of accuracy and reliability of active geodetic network for monitoring the Dniester HPP. Geodesy, cartography and aerial photography. (79), 1-14 (In Ukrainian).
12. Tretyak, K., Savchyn, I., Zayats, O., Golubinka, Yu., Lompas, O., & Bisovetsky, Yu. (2017). Installation and maintenance of automated systems for control of spatial displacements of engineering structures of Ukrainian hydroelectric power plants. Hydropower of Ukraine, (1-2), 33-41 (In Ukrainian).
13. Wu, D., Yan, H., & Shen, Y. (2017). TSAnalyzer, a GNSS time series analysis software. Gps Solutions, 21(3), 1389-1394.