Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

[1]  Alessandro Capra,et al.  Geodetic and Geophysical Observations in Antarctica , 2008 .

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

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

[4]  Susanna K. Ebmeier,et al.  Application of independent component analysis to multitemporal InSAR data with volcanic case studies , 2016 .

[5]  H. Kaiser,et al.  A Study Of A Measure Of Sampling Adequacy For Factor-Analytic Correlation Matrices. , 1977, Multivariate behavioral research.

[6]  Peter Steigenberger,et al.  Improved Constraints on Models of Glacial Isostatic Adjustment: A Review of the Contribution of Ground-Based Geodetic Observations , 2010 .

[7]  Ian M. Howat,et al.  GPS measurements of crustal uplift near Jakobshavn Isbræ due to glacial ice mass loss , 2010 .

[8]  Jürgen Kusche,et al.  Separation of global time-variable gravity signals into maximally independent components , 2012, Journal of Geodesy.

[9]  Corné Kreemer,et al.  What caused the March 25, 1998 Antarctic Plate earthquake?: Inferences from regional stress and strain rate fields , 2000 .

[10]  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 .

[11]  Joseph L. Awange,et al.  Independent patterns of water mass anomalies over Australia from satellite data and models , 2012 .

[12]  H. Akaike.  A new look at the statistical model identification , 1974 .

[13]  Thomas A. Herring,et al.  Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008 , 2011 .

[14]  Enrico Serpelloni,et al.  Blind source separation problem in GPS time series , 2016, Journal of Geodesy.

[15]  Wu Chen,et al.  Characteristics of Daily Position Time Series from the Hong Kong Gps Fiducial Network , 2008 .

[16]  Donald A. Jackson,et al.  How many principal components? stopping rules for determining the number of non-trivial axes revisited , 2005, Comput. Stat. Data Anal..

[17]  John J. Wang,et al.  Upgrade of three municipal wastewater treatment lagoons using a high surface area media , 2012, Frontiers of Environmental Science & Engineering.

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

[19]  Marie-Noëlle Bouin,et al.  New constraints on Antarctic plate motion and deformation from GPS data , 2000 .

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

[21]  Qu Xiaochuan,et al.  Noise Model Establishment and Analysis of IGS Reference Station Coordinate Time Series inside China , 2012 .

[22]  Wujiao Dai,et al.  Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis , 2015, Earth, Planets and Space.

[23]  Peng Yuan,et al.  Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China , 2018, Remote. Sens..

[24]  Geoffrey Blewitt,et al.  Terrestrial reference frame NA12 for crustal deformation studies in North America , 2013 .

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

[26]  Andrea Morelli,et al.  Seismological imaging of the Antarctic continental lithosphere: a review , 2004 .

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

[28]  Bofeng Li,et al.  Weighted spatiotemporal filtering using principal component analysis for analyzing regional GNSS position time series , 2015, Acta Geodaetica et Geophysica.

[29]  H. Kaiser.  An index of factorial simplicity , 1974 .

[30]  Jeffrey T. Freymueller,et al.  Global Plate Velocities from the Global Positioning System , 1997 .

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

[32]  Tieding Lu,et al.  Accuracy enhancement of GPS time series using principal component analysis and block spatial filtering , 2015 .

[33]  T. Schneider.  Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values. , 2001 .

[34]  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.

[35]  Georgios Maniatis,et al.  Three-dimensional numerical modeling of slip rate variations on normal and thrust fault arrays during ice cap growth and melting , 2009 .

[36]  Geoffrey Blewitt,et al.  Rise of the Ellsworth mountains and parts of the East Antarctic coast observed with GPS , 2011 .

[37]  Matt A. King,et al.  Ongoing deformation of Antarctica following recent Great Earthquakes , 2016 .

[38]  Peter J. Clarke,et al.  Rapid bedrock uplift in the Antarctic Peninsula explained by viscoelastic response to recent ice unloading , 2014 .

[39]  Wujiao Dai,et al.  Extracting seasonal deformations of the Nepal Himalaya region from vertical GPS position time series using Independent Component Analysis , 2017 .

[40]  Peter J. Clarke,et al.  Widespread low rates of Antarctic glacial isostatic adjustment revealed by GPS observations , 2011 .

[41]  Michael G. Sideris,et al.  Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data , 2014 .

[42]  Wujiao Dai,et al.  Common mode error in Antarctic GPS coordinate time-series on its effect on bedrock-uplift estimates , 2018 .

[43]  John Langbein,et al.  Noise in GPS displacement measurements from Southern California and Southern Nevada , 2008 .

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

[45]  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 .

[46]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.