Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China

Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental loading effects, and the estimation of a realistic and unbiased GPS velocity field for geodynamic applications. In this paper, we homogeneously processed the daily observations of 231 Crustal Movement Observation Network of China (CMONOC) Continuous GPS stations to obtain their position time series. Then, we filtered out the CMC and evaluated its effects on the periodic signals and noise for the CMONOC time series. Results show that, with CMC filtering, peaks in the stacked power spectra can be reduced at draconitic harmonics up to the 14th, supporting the point that the draconitic signal is spatially correlated. With the colored noise suppressed by CMC filtering, the velocity uncertainty estimates for both of the two subnetworks, CMONOC-I (≈16.5 years) and CMONOC-II (≈4.6 years), are reduced significantly. However, the CMONOC-II stations obtain greater reduction ratios in velocity uncertainty estimates with average values of 33%, 38%, and 54% for the north, east, and up components. These results indicate that CMC filtering can suppress the colored noise amplitudes and improve the precision of velocity estimates. Therefore, a unified, realistic, and three-dimensional CMONOC GPS velocity field estimated with the consideration of colored noise is given. Furthermore, contributions of environmental loading to the vertical CMC are also investigated and discussed. We find that the vertical CMC are reduced at 224 of the 231 CMONOC stations and 170 of them are with a root mean square (RMS) reduction ratio of CMC larger than 10%, confirming that environmental loading is one of the sources of CMC for the CMONOC height time series.

[1]  Zhi-Xun Shen,et al.  Contemporary Crustal Deformation Around Southeast Borderland of Tibetan Plateau , 2004 .

[2]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake , 1997 .

[3]  U. Hugentobler,et al.  Impact of Earth radiation pressure on GPS position estimates , 2012, Journal of Geodesy.

[4]  Yehuda Bock,et al.  Spatiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regional GPS network analysis , 2006 .

[5]  Janusz Bogusz,et al.  On the combined effect of periodic signals and colored noise on velocity uncertainties , 2017, GPS Solutions.

[6]  Peng Yuan,et al.  Influences of Environmental Loading Corrections on the Nonlinear Variations and Velocity Uncertainties for the Reprocessed Global Positioning System Height Time Series of the Crustal Movement Observation Network of China , 2018, Remote. Sens..

[7]  Jin Ma,et al.  Active tectonic blocks and strong earthquakes in the continent of China , 2003, Science in China Series D Earth Sciences.

[8]  Hans-Peter Plag,et al.  A continuous GPS coordinate time series analysis strategy for high-accuracy vertical land movements , 2008 .

[9]  A. Amiri-Simkooei,et al.  Multivariate analysis of GPS position time series of JPL second reprocessing campaign , 2017, Journal of Geodesy.

[10]  Bin Zhao,et al.  Vertical Displacements Driven by Groundwater Storage Changes in the North China Plain Detected by GPS Observations , 2018, Remote. Sens..

[11]  W. Gan,et al.  Three‐dimensional velocity field of present‐day crustal motion of the Tibetan Plateau derived from GPS measurements , 2013 .

[12]  Bofeng Li,et al.  Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis , 2013, Journal of Geodesy.

[13]  H. Schuh,et al.  Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium‐Range Weather Forecasts operational analysis data , 2006 .

[14]  Michael Bevis,et al.  Trajectory models and reference frames for crustal motion geodesy , 2014, Journal of Geodesy.

[15]  Geoffrey Blewitt,et al.  Effect of annual signals on geodetic velocity , 2002 .

[16]  Qi Wang,et al.  Noise analysis of continuous GPS coordinate time series for CMONOC , 2012 .

[17]  Yann Klinger,et al.  Coseismic deformation of the 2001 Mw = 7.8 Kokoxili earthquake in Tibet, measured by synthetic aperture radar interferometry , 2005 .

[18]  Rolf Dach,et al.  GNSS related periodic signals in coordinate time-series from Precise Point Positioning , 2017 .

[19]  Yehuda Bock,et al.  High‐rate real‐time GPS network at Parkfield: Utility for detecting fault slip and seismic displacements , 2004 .

[20]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[21]  T. Dixon,et al.  Noise in GPS coordinate time series , 1999 .

[22]  Yidong Lou,et al.  Crustal Deformation in the India‐Eurasia Collision Zone From 25 Years of GPS Measurements , 2017 .

[23]  Liu Jingnan.  Present-day Crustal Movement Speed Field of China Continent Block Using Local Repeated GPS Network , 2001 .

[24]  J. Johansson,et al.  Continuous GPS measurements of postglacial adjustment in Fennoscandia 1. Geodetic results , 2002 .

[25]  Geoffrey Blewitt,et al.  A geodetic plate motion and Global Strain Rate Model , 2014 .

[26]  Enrico Serpelloni,et al.  Vertical GPS ground motion rates in the Euro‐Mediterranean region: New evidence of velocity gradients at different spatial scales along the Nubia‐Eurasia plate boundary , 2013 .

[27]  J. Scargle.  Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .

[28]  J. Ray,et al.  Anomalous harmonics in the spectra of GPS position estimates , 2008 .

[29]  Matt A. King,et al.  Long GPS coordinate time series: Multipath and geometry effects , 2009 .

[30]  Paul Tregoning,et al.  Atmospheric effects and spurious signals in GPS analyses , 2009 .

[31]  Göran Ekström,et al.  The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes , 2012 .

[32]  Jing-nan Liu,et al.  Present-Day Crustal Deformation in China Constrained by Global Positioning System Measurements , 2001, Science.

[33]  R. Nikolaidis.  Observation of geodetic and seismic deformation with the Global Positioning System , 2002 .

[34]  D. McLaughlin,et al.  Crustal motions in Great Britain: evidence from continuous GPS, absolute gravity and Holocene sea level data , 2009 .

[35]  C. Demets,et al.  Crustal velocity field of Mexico from continuous GPS measurements, 1993 to June 2001: Implications for the neotectonics of Mexico , 2003 .

[36]  Simon D. P. Williams,et al.  Fast error analysis of continuous GNSS observations with missing data , 2013, Journal of Geodesy.

[37]  Yuanxi Yang,et al.  Spatiotemporal filtering for regional GPS network in China using independent component analysis , 2017, Journal of Geodesy.

[38]  Alireza Amiri-Simkooei,et al.  On the nature of GPS draconitic year periodic pattern in multivariate position time series , 2013 .

[39]  Wang Min,et al.  Effects of non-tectonic crustal deformation on continuous GPS position time series and correction to them , 2005 .

[40]  John H. Woodhouse,et al.  Determination of earthquake source parameters from waveform data for studies of global and regional seismicity , 1981 .

[41]  Janusz Bogusz,et al.  On the significance of periodic signals in noise analysis of GPS station coordinates time series , 2016, GPS Solutions.

[42]  John Langbein,et al.  Estimating rate uncertainty with maximum likelihood: differences between power-law and flicker–random-walk models , 2012, Journal of Geodesy.

[43]  William H. Press,et al.  Numerical Recipes in Fortran 77 , 1992 .

[44]  N. Lomb.  Least-squares frequency analysis of unequally spaced data , 1976 .

[45]  Yunfeng Tian,et al.  Extracting the regional common‐mode component of GPS station position time series from dense continuous network , 2016 .

[46]  Yehuda Bock,et al.  Error analysis of continuous GPS position time series , 2004 .

[47]  Zhao Li,et al.  Estimates of Minor Ocean Tide Loading Displacement and Its Impact on Continuous GPS Coordinate Time Series , 2014, Sensors.

[48]  Tomokazu Kobayashi,et al.  Coseismic and postseismic slip of the 2011 magnitude-9 Tohoku-Oki earthquake , 2011, Nature.

[49]  Janusz Bogusz,et al.  Error analysis for European IGS stations , 2015, Studia Geophysica et Geodaetica.

[50]  Wei Wang,et al.  Crustal deformation on the Chinese mainland during 1998–2014 based on GPS data , 2015 .

[51]  Nico Sneeuw,et al.  Annual variations of monsoon and drought detected by GPS: A case study in Yunnan, China , 2017, Scientific Reports.

[52]  O. Francis,et al.  Modelling the global ocean tides: modern insights from FES2004 , 2006 .

[53]  Yehuda Bock,et al.  GPS measurements of current crustal movements along the Dead Sea Fault , 2004 .

[54]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Error analysis of daily position estimates and site velocities , 1997 .

[55]  Mike P. Stewart,et al.  GPS height time series: Short‐period origins of spurious long‐period signals , 2007 .

[56]  A. Chulliat,et al.  International Geomagnetic Reference Field: the eleventh generation , 2010 .

[57]  Geoffrey Blewitt,et al.  Evidence for Deep Magma Injection Beneath Lake Tahoe, Nevada-California , 2004, Science.

[58]  Wei Wang,et al.  Present-day velocity field and block kinematics of Tibetan Plateau from GPS measurements , 2017 .

[59]  Peizhen Zhang,et al.  Continuous deformation of the Tibetan Plateau from global positioning system data , 2004 .

[60]  Mike P. Stewart,et al.  Aliased tidal signatures in continuous GPS height time series , 2003 .

[61]  R. Dill,et al.  Numerical simulations of global‐scale high‐resolution hydrological crustal deformations , 2013 .

[62]  Liansheng Deng,et al.  Quantitative analysis of geophysical sources of common mode component in CMONOC GPS coordinate time series , 2017 .