Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

Weijie Tan,Xueqing Xu,Weijing Qu,Junping Chen,Danan Dong

doi:10.3390/rs12050751

Abstract

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

Highlights

The dense GNSS (Global Navigation Satellite System) networks provide detailed information to explore the crustal deformation, such as the secular tectonic movements, the co- and postseismic displacements, the loading effects caused by mass redistributions, the thermal expansion, and other unknown geophysical processes [1,2,3,4,5]
We focus on common mode component (CMC) of site displacements caused by mass loadings and try to explore the Common mode error (CME) origin in Chuandian region
We use the criterion from Dong et al [12] to define the CME as the mode for which most sites have significant normalized responses, and the eigenvalues of this mode exceed 1% of the summation of all eigenvalues

Summary

Introduction

The dense GNSS (Global Navigation Satellite System) networks provide detailed information to explore the crustal deformation, such as the secular tectonic movements, the co- and postseismic displacements, the loading effects caused by mass redistributions, the thermal expansion, and other unknown geophysical processes [1,2,3,4,5]. The regional GNSS network analyses provide ideal means to detect and isolate the CME and to enhance the signal-to-noise ratio of the solution series Several approaches, such as stacking, principal component analysis (PCA), independent component analysis (ICA), etc., have been proposed in GNSS position time series analysis to estimate the CME [7,8,9,10,11,12,13,14,15,16,17]. Liu et al [13] applied the ICA to analyze continuous GPS data in Chuandian region and discussed the mechanism of the CME with 40 sites They explained that the seasonal variations in CME were mainly due to loading effects of the surface atmosphere and soil moisture loadings. We evaluate the corrected CME in the GPS residual time series which have subtracted the calculated daily site displacements caused by atmospheric and soil moisture mass loadings. We demonstrate the contributions of the two mass loadings to the CME in Chuandian region

Data Source and CME Processing Methods

Acquisition of GPS Residual Coordinate Time Series

Results

Daily Soil Moisture Mass Loading