Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis

Abstract

Continuous Global Positioning Systems (GPS) coordinate time series with a high spatiotemporal resolution, and provide a great opportunity to study their noise models and common mode errors (CMEs), thus making it possible to detect and analyse spatiotemporal characteristics of tectonic and non-tectonic signals in time series, and further to estimate a reliable and accurate velocity field of crustal movement in a region by removing CMEs and using the optimal noise model. In this paper, we used GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC) with an approximate decadal period from 2010 to 2020, to construct optimal noise models by fitting them with several noise combinations according to the Akaike information criterion (AIC). We further adopted independent component analysis (ICA) to extract CMEs and analysed their spatiotemporal characteristics, and then evaluated their effects on noise models and velocity uncertainties, and finally estimated a decennial velocity field of crustal movement with a higher signal-to-noise ratio (SNR) by applying the CME filtering and considering the optimal noise model in Mainland China. Our results show that optimal noise models are dominated by white noise (WN) plus flicker noise (FN) for both east and north components, and WN plus power law noise (PN) with spectral index close to −1 for up component, respectively. ICA filtering can remove the highly spatially correlated CMEs and decrease the mean RMSEs of the residual time series by about 40–60%, providing a more accurate velocity field with a higher SNR in Mainland China, accordingly.

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

“…If a cleaner time series is desired, noise models need to be considered in addition to filtered CME [15]. In the future, the rationality of using the PCA method can also be investigated by comparing the percentage of components with the corresponding seasonal variations and load model errors [11,14].…”

“…China is located at the southeastern part of the Eurasian plate and is affected by the eastward subduction of the western Pacific and Philippine plates and the collision of the Indian and Eurasian plates to the west and southwest, respectively, with complex and diverse crustal motions and deformations [11]. Yu et al established an accurate contemporary horizontal velocity field of the Eurasian plate in the China region based on data from hundreds of CORS sites from 1998 to 2018 [12].…”

“…In the future, the rationality of using the PCA method can also be investigated by comparing the percentage of components with the corresponding seasonal variations and load model errors [11,14].…”

An improved stacking filtering for extracting the common-mode errors on GNSS coordinate time series in Shanxi

“…reduced accordingly (Gruszczynski et al, 2016;Zhou et al, 2020Zhou et al, , 2022. Figure 6 shows the interstation correlation coefficients before and after removal of CME from the residual time series for the north (left panel), east (middle panel) and up (right panel) components, where the lower triangle indicates the correlation coefficient between stations before CME removal and the upper triangle indicates the correlation coefficient between stations after CME removal.…”

“…Since establishing the Crustal Movement Observation Network of China (CMONOC), a large amount of observational data has been accumulated, including coordinate time series data of continuously operating reference stations (Li et al, 2012;Hein, 2020;Zhou et al, 2022). The coordinate time series are defined as a set of coordinates of a reference station arranged in chronological order.…”