Blewitt, G., Kreemer, C., Hammond, W. C., Plag, H. P., Stein, S., and Okal, E.: Rapid determination of earthquake magnitude using GPS for tsunami warning systems, Geophys. Res. Lett., 33, L11309, https://doi.org/10.1029/2006GL026145, 2006.
Bos, M. S., Fernandes, R. M. S., Williams, S. D. P., and Bastos, L.: Fast error analysis of continuous GPS observations, J. Geodesy, 82, 157–166, https://doi.org/10.1007/s00190-007-0165-x, 2008.
Bos, M. S., Bastos, L., and Fernandes, R. M. S.: The influence of seasonal signals on the estimation of the tectonic motion in short continuous GPS time-series, J. Geodyn., 49, 205–209, https://doi.org/10.1016/j.jog.2009.10.005, 2010.
Cai, F.: Study on global distribution laws and partial mechanisms of nonlinear variations of GNSS stations' coordinates, M.S. thesis, Strategic Support Force Information Engineering University, https://doi.org/10.27188/d.cnki.gzjxu.2020.000068, 2020 (in Chinese).
Calais, E., Gonzalez, O. F., Arango-Arias, E. D., Moreno, B., Palau, R., Cutie, M., Diez, E., Montenegro, C., Rodriguez Roche, E., Garcia, J., Castellanos, E., and Symithe, S.: Current deformation along the northern Caribbean plate boundary from GNSS measurements in Cuba, Tectonophysics, 868, 230068, https://doi.org/10.1016/j.tecto.2023.230068, 2023.
Chen, B., Bian, J., Ding, K., Wu, H., and Li, H.: Extracting seasonal signals in GNSS coordinate time series via weighted nuclear norm minimization, Remote. Sens.-Basel, 12, 2027, https://doi.org/10.3390/rs12122027, 2020.
Cheng, J., Yu, D., and Yu, Y.: Research on the intrinsic mode function (IMF) criterion in EMD method, Mech. Syst. Signal. Pr., 20, 817–824, https://doi.org/10.1016/j.ymssp.2005.09.011, 2006.
Costantino, G., Giffard-Roisin, S., Radiguet, M., Dalla Mura, M., Marsan, D., and Socquet, A.: Multi-station deep learning on geodetic time series detects slow slip events in Cascadia, Commun. Earth Environ., 4, 435, https://doi.org/10.1038/s43247-023-01107-7, 2023.
Davis, J. L., Wernicke, B. P., and Tamisiea, M. E.: On seasonal signals in geodetic time series, J. Geophys. Res., 117, B01403, https://doi.org/10.1029/2011JB008690, 2012.
Din, A. H. M., Zulkifli, N. A., Hamden, M. H., and Wan Aris, W. A.: Sea level trend over Malaysian seas from multi-mission satellite altimetry and vertical land motion corrected tidal data, Adv. Space. Res., 63, 3452–3472, https://doi.org/10.1016/j.asr.2019.02.022, 2019.
Dong, D., Fang, P., Bock, Y., Cheng, M. K., and Miyazaki, S.: Anatomy of apparent seasonal variations from GPS-derived site position time series, J. Geophys. Res., 107, ETG 9-1–ETG 9-16, https://doi.org/10.1029/2001JB000573, 2002.
Gazeaux, J., Williams, S., King, M., Bos, M., Dach, R., Deo, M., Moore, A. W., Ostini, L., Petrie, E., Roggero, M., Teferle, F. N., Olivares, G., and Webb, F. H.: Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment, J. Geoghys. Res.-Sol. Ea., 118, 2397–2407, https://doi.org/10.1002/jgrb.50152, 2013.
Griffiths, J. and Ray, J.: Impacts of GNSS position offsets on global frame stability, Geophys. J. Int., 204, 480–487, https://doi.org/10.1093/gji/ggv455, 2016.
Gülal, E., Erdoğan, H., and Tiryakioğlu, İ.: Research on the stability analysis of GNSS reference stations network by time series analysis, Digit. Signal. Process., 23, 1945–1957, https://doi.org/10.1016/j.dsp.2013.06.014, 2013.
He, X., Montillet, J. P., Fernandes, R., Bos, M., Yu, K., Hua, X. and Jiang, W.: Review of current GPS methodologies for producing accurate time series and their error sources, J. Geodyn., 106, 12–29, https://doi.org/10.1016/j.jog.2017.01.004, 2017.
Hetland, E. A. and Hager, B. H.: The effects of rheological layering on post-seismic deformation, Geophys. J. Int., 166, 277–292, https://doi.org/10.1111/j.1365-246X.2006.02974.x, 2006.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N-C. Tung, C. C. and Liu, H. H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, P. Roy. Soc. Lond. Ser. A-Math., 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998.
Huang, N. E., Shen, Z., and Long, S. R.: A new view of nonlinear water waves: the Hilbert spectrum, Annu. Rev. Fluid. Mech., 31, 417–457, https://doi.org/10.1146/annurev.fluid.31.1.417, 1999.
Huang, Y., Schmitt, F. G., Lu, Z., and Liu, Y.: Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis, J. Hydrol., 373, 103–111, https://doi.org/10.1016/j.jhydrol.2009.04.015, 2009.
Ide, S. and Tanaka, Y.: Controls on plate motion by oscillating tidal stress: Evidence from deep tremors in western Japan, Geophys. Res. Lett., 41, 3842–3850, https://doi.org/10.1002/2014GL060035, 2014.
Johnson, C. W., Lau, N., and Borsa, A.: An assessment of global positioning system velocity uncertainty in California, Earth Space Sci., 8, e2020EA001345, https://doi.org/10.1029/2020EA001345, 2021.
Kennedy, J., Dunn, R., McCarthy, M., Titchner, H., and Morice, C.: Global and regional climate in 2016, Weather, 72, 219–225, https://doi.org/10.1002/wea.3042, 2017.
Klos, A., Olivares, G., Teferle, F. N., Hunegnaw, A., and Bogusz, J.: On the combined effect of periodic signals and colored noise on velocity uncertainties, GPS Solut., 22, 1, https://doi.org/10.1007/s10291-017-0674-x, 2018.
Kopsinis, Y. and McLaughlin, S.: Improved EMD using doubly-iterative sifting and high order spline interpolation, Eurasip. J. Adv. Sig. Pr., 2008, 128293, https://doi.org/10.1155/2008/128293, 2008.
Li, W.: Data adaptive analysis on vertical surface deformation derived from daily ITSG-Grace2018 model, Sensors, 20, 4477, https://doi.org/10.3390/s20164477, 2020.
Li, W. and Shen, Y.: Detection and analysis of velocity and amplitude changes in GNSS coordinate sequences, Journal of Tongji University, 42, 604–610, https://doi.org/10.3969/j.issn.0253-374x.2014.04.017, 2014 (in Chinese).
Liu, X., Chen, W., and Mao, A.: An adaptive optimization EEMD method and its application in bearing fault detection, ResearchSquare [preprint], https://doi.org/10.21203/rs.3.rs-2615109/v1, 28 February 2023.
Munekane, H.: Modeling long-term volcanic deformation at Kusatsu-Shirane and Asama volcanoes, Japan, using the GNSS coordinate time series, Earth. Planets Space, 73, 192, https://doi.org/10.1186/s40623-021-01512-2, 2021.
Nishimura, T.: Pre-, co-, and post-seismic deformation of the 2011 Tohoku-oki earthquake and its implication to a paradox in short-term and long-term deformation, J. Disaster Res., 9, 294–302, https://doi.org/10.20965/jdr.2014.p0294, 2014.
Peng, W., Dai, W. J., Santerre, R., Cai, C. S., and Kuang, C. L.: GNSS vertical coordinate time series analysis using single-channel independent component analysis method, Phys. Eng. Sci., 454, 903–995, https://doi.org/10.1007/s00024-016-1309-9, 2018.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical Recipes: The Art of Scientific Computing, 3rd Edn., Cambridge University Press, ISBN 9780521880688, 2010.
Qiu, X., Wang, F., Zhou, Y., and Zhou, S.: Iteration empirical mode decomposition method for filling the missing data of GNSS position time series, Acta. Geodyn. Geomater., 19, 271–279, https://doi.org/10.13168/AGG.2022.0012, 2022.
Rodionov, N. S.: A sequential algorithm for testing climate regime shifts, Geophys. Res. Lett., 31, L09204, https://doi.org/10.1029/2004GL019448, 2004.
Ran, J., Bian, J., Chen, G., Zhang, Y., and Liu, W.: A truncated nuclear norm regularization model for signal extraction from GNSS coordinate time series, Adv. Space Res., 70, 336–349, https://doi.org/10.1016/j.asr.2022.04.040, 2022.
Ray, J., Altamimi, Z., Collilieux, X., and Van Dam, T.: Anomalous harmonics in the spectra of GPS position estimates, GPS Solut., 12, 55–64, https://doi.org/10.1007/s10291-007-0067-7, 2008.
Scaramuzza, S., Dach, R., Beutler, G., Arnold, D., Sušnik, A., and Jäggi, A.: Dependency of geodynamic parameters on the GNSS constellation, J. Geodesy, 92, 93–104, https://doi.org/10.1007/s00190-017-1047-5, 2017.
Shen, Y., Li, W., Xu, G., and Li, B.: Spatiotemporal filtering of regional GNSS network's position time series with missing data using principle component analysis, J. Geodesy, 88, 1–12, https://doi.org/10.1007/s00190-013-0663-y, 2014.
Singh, A., Reager, J. T., and Behrangi, A.: Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit, Hydrol. Earth Syst. Sci., 25, 511–526, https://doi.org/10.5194/hess-25-511-2021, 2021.
Sorin, N., NorbertSzabolcs, S., Ahmed, E., Michal, A., Zinovy, M., Ilie, N. E., Jacek, K., and Kamil, M.: Implication between geophysical events and the variation of seasonal signal determined in GNSS position time series, Remote. Sens.-Basel, 13, 3478–3478, https://doi.org/10.3390/RS13173478, 2021.
Su, L., Zhai, H., Wang, Q., and Tian, X.: Detecting offsets in GPS coordinate time series based on SSA method, Journal of Geodesy and Geodynamics, 43, 464–466, https://doi.org/10.14075/j.jgg.2023.05.005, 2023 (in Chinese).
Tehranchi, R., Moghtased-Azar, K., and Safari, A.: A new statistical test based on the WR for detecting offsets in GPS experiment, Earth Space Sci., 7, e2019EA000810, https://doi.org/10.1029/2019EA000810, 2020.
Tobita, M.: Combined logarithmic and exponential function model for fitting postseismic GNSS time series after 2011 Tohoku-Oki earthquake, Earth. Planets Space, 68, 1–12, 2016.
Van Dam, T., Wahr, J., Milly, P. C. D., Shmakin, A. B., Blewitt, G., Lavallée, D., and Larson, K. M.: Crustal displacements due to continental water loading, Geophys. Res. Lett., 28, 651–654, https://doi.org/10.1029/2000GL012120, 2001.
Wang, J., Ding, K., Sun, H., Zhang, G., and Chen, X.: Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation, Geodesy and Geodynamics, 14, 573–581, https://doi.org/10.1016/j.geog.2023.07.002, 2023.
Wang, K., Jiang, W., Chen, H., An, X., Zhou, X., Yuan, P., and Chen, Q.: Analysis of seasonal signal in GPS short-baseline time series, Pure. Appl. Geophys., 175, 3485–3509, https://doi.org/10.1007/s00024-018-1871-4, 2018.
Wang, L. and Herring, T.: Impact of estimating position offsets on the uncertainties of GNSS site velocity estimates, J. Geophys. Res.-Sol. Ea., 124, 13452–13467, https://doi.org/10.1029/2019JB017705, 2019.
Willen, M. O., Horwath, M., Groh, A., Helm, V., Uebbing, B., and Kusche, J.: Feasibility of a global inversion for spatially resolved glacial isostatic adjustment and ice sheet mass changes proven in simulation experiments, J. Geodesy, 96, 75, https://doi.org/10.1007/S00190-022-01651-8, 2022.
Wu, F. and Qu, L.: An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery, J. Sound. Vib., 314, 586–602, https://doi.org/10.1016/j.jsv.2008.01.020, 2008.
Wu, Z. and Huang, N. E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1, 1–41, https://doi.org/10.1142/S1793536909000047, 2009.
Zaccagnino, D., Vespe, F., and Doglioni, C.: Tidal modulation of plate motions, Earth-Sci. Rev., 205, 103179, https://doi.org/10.1016/j.earscirev.2020.103179, 2020.
Zhou, X., Yang, Y., Chen, Q., Fan, W., and Ma, Y.: A robust trend estimator for GNSS time series in the presence of complex periodicity and its evaluation on multi-source products of IGS and IGMAS, GPS Solut., 26, 103, https://doi.org/10.1007/s10291-022-01271-x, 2022.
