With the improvement of Global Navigation Satellite System (GNSS) observation accuracy and the establishment of large continuously operating networks, long GNSS time series are now widely used to understand a range of Earth deformation processes. The continuously operating stations of the Crustal Movement Observation Network of China capture deformation signals due to time-dependent tectonic, nontectonic mass loading, and potentially unknown geophysical processes. In order to separate and recover these underlying sources accurately and effectively, we apply the independent component analysis (ICA) to decompose the observed time series of vertical displacements. Then, we compare these signals with those predicted from independently developed geophysical process models of atmospheric, nontidal ocean, snow, soil moisture mass loading, and the Land Surface Discharge Model, as well as with Gravity Recovery and Climate Experiment observations. For comparison, we also perform the principal component analysis decomposition of time series and find that the ICA achieves a more consistent representation of multiple geophysical contributors to annual vertical GNSS displacements. ICA can decompose the long-term trend and different seasonal and multiannual signals that closely correspond to the independently derived mass loading models. We find that independent contributions from atmospheric, soil moisture, and snow mass loading can be resolved from the GNSS data. Discrepancies are likely due to the correlated nature of some of the loading processes and unmodeled contributions from groundwater and surface water changes in South Central China and the Ganges Basin.

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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.

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