Separation of Sources of Seasonal Uplift in China Using Independent Component Analysis of GNSS Time Series

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.