GNSS Vertical Coordinate Time Series Analysis Using Single-Channel Independent Component Analysis Method

Daily vertical coordinate time series of Global Navigation Satellite System (GNSS) stations usually contains tectonic and non-tectonic deformation signals, residual atmospheric delay signals, measurement noise, etc. In geophysical studies, it is very important to separate various geophysical signals from the GNSS time series to truthfully reflect the effect of mass loadings on crustal deformation. Based on the independence of mass loadings, we combine the Ensemble Empirical Mode Decomposition (EEMD) with the Phase Space Reconstruction-based Independent Component Analysis (PSR-ICA) method to analyze the vertical time series of GNSS reference stations. In the simulation experiment, the seasonal non-tectonic signal is simulated by the sum of the correction of atmospheric mass loading and soil moisture mass loading. The simulated seasonal non-tectonic signal can be separated into two independent signals using the PSR-ICA method, which strongly correlated with atmospheric mass loading and soil moisture mass loading, respectively. Likewise, in the analysis of the vertical time series of GNSS reference stations of Crustal Movement Observation Network of China (CMONOC), similar results have been obtained using the combined EEMD and PSR-ICA method. All these results indicate that the EEMD and PSR-ICA method can effectively separate the independent atmospheric and soil moisture mass loading signals and illustrate the significant cause of the seasonal variation of GNSS vertical time series in the mainland of China.

[1]  D. Grech,et al.  On the scaling ranges of detrended fluctuation analysis for long-term memory correlated short series of data , 2012, 1206.1007.

[2]  Kelin Wang,et al.  A Silent Slip Event on the Deeper Cascadia Subduction Interface , 2001, Science.

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

[4]  P. Teunissen,et al.  Assessment of noise in GPS coordinate time series : Methodology and results , 2007 .

[5]  Manfred Schroeder,et al.  Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise , 1992 .

[6]  Jose Alvarez-Ramirez,et al.  1/fα fractal noise generation from Grünwald–Letnikov formula , 2009 .

[7]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[8]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

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

[10]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

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

[12]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

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

[14]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[15]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[16]  Kegen Yu,et al.  Extracting White Noise Statistics in GPS Coordinate Time Series , 2012, IEEE Geoscience and Remote Sensing Letters.

[17]  Jean-François Cardoso,et al.  Multidimensional independent component analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[18]  X. Zou,et al.  Comparison between linear and nonlinear trends in NOAA-15 AMSU-A brightness temperatures during 1998–2010 , 2012, Climate Dynamics.

[19]  J. B. Rundle,et al.  Using Eigenpattern Analysis to Constrain Seasonal Signals in Southern California , 2004 .

[20]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[21]  Wujiao Dai,et al.  A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis , 2015 .

[22]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

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

[24]  M. Heflin,et al.  Atmospheric pressure loading effects on Global Positioning System coordinate determinations , 1994 .

[25]  Hua Liao,et al.  GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications , 2014 .

[26]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

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