Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis

Bin Liu,Xiaolin Meng,Wei Peng,Wujiao Dai

doi:10.1186/s40623-015-0357-1

Bin Liu, Xiaolin Meng + Show 2 more

Open Access

https://doi.org/10.1186/s40623-015-0357-1

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Abstract

GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis.

Highlights

With the improvement of positioning accuracy and the growing number of CORS, GPS has found an increasingly wide utilization in the field of geodesy and geodynamics, such as earthquake studies (Segall and Davis 1997), volcano deformation monitoring (Ueda et al 2013), crustal movement research (Niu et al 2005; Teferle et al 2008), and so on
We suggest that the non-tectonic deformation, mainly including the atmospheric and soil moisture mass loading, is the main component for the common-mode error (CME) in GPS networks, and it can be reflected by the independent components (ICs) extracted by Independent component analysis (ICA) spatiotemporal filtering
The results show that ICA is an effective spatiotemporal filtering method as Principal component analysis (PCA) to remove to CME in GPS networks and improve the signal-to-noise ratio (SNR) of observation data

Summary

Introduction

With the improvement of positioning accuracy and the growing number of CORS, GPS has found an increasingly wide utilization in the field of geodesy and geodynamics, such as earthquake studies (Segall and Davis 1997), volcano deformation monitoring (Ueda et al 2013), crustal movement research (Niu et al 2005; Teferle et al 2008), and so on. By analyzing the independent components with spatial responses and the non-tectonic signals, we could precisely define the CME of GPS networks and make right interpretations to the common mode independent components (ICs) extracted by ICA. When using ICA, we define the common mode through the spatial responses of the ICs. Case study Vertical GPS displacement series in two representative regions, the UK and Sichuan-Yunnan region in China, are applied in the spatiotemporal filtering using PCA and ICA. The physical source of CME in vertical time series was discussed by analyzing the temporal components with their spatial responses in filtering and the main non-tectonic signals with their spatial distribution. Both these two methods can improve the SNR of residual error time series effectively.

Method

Conclusion