Spatiotemporal filtering for regional GPS network in China using independent component analysis

Feng, Meng; Yang, Yuanxi; Zeng, Anmin; Zhao, Bin

doi:10.1007/s00190-016-0973-y

Cited by 53 publications

(78 citation statements)

References 53 publications

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“…With the removal of CME, RMS reduced by about 6.3% for the three components of the whole CMONOC network in China [3]. Relatively speaking, our study region was small, the CME was significant, and the RMS reductions were around 18% for North and Up components and 15% for the East component derived from IPCA.…”

Section: Discussionmentioning

confidence: 68%

“…In previous studies, different methods have been employed, such as Stacking [17], PCA [1,21] and ICA [3]. The observations in these studies were regarded as equally weighted, while in fact, the formal errors were different.…”

Section: Discussionmentioning

confidence: 99%

“…whereŜ p is the CME extracted with PCA;Ŝ I is the CME extracted with IPCA; and P is the weight for each epoch in each site in Equation (3). Since the weight is determined by Equations (4) and (5), average errors computed by Equations (12) and (13) are comparable.…”

Section: Real Gnss Position Time Series Analysismentioning

confidence: 99%

“…Spatial-correlated Common Mode Error (CME) is significantly present in regional GNSS networks [1][2][3]. Due to its existence, especially when the CME is larger than the transient signals, it obscures the detection of transient signals such as slow slip events and episodic tremors.…”

Section: Introductionmentioning

confidence: 99%

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The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Shen

2018

Remote Sensing

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Abstract:In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations' coordinate components are calculated. However, spatiotemporal filtering based on traditional Principal Component Analysis (PCA) for regional GNSS position time series does not take these formal errors into account. This paper developed a PCA-based approach to extract Common Mode Error (CME) from the position time series of a regional GNSS station network, where formal errors were applied to construct a weight factor. Because coordinate components with larger errors have smaller weight factors in extracting CME, the performance of our proposed approach was anticipated to be better than the traditional PCA approach. The position time series of 25 stations in the Yunnan Province, China, were analyzed using our approach, as well as the traditional PCA approach. The average errors of the residual time series after removing the CMEs with our approach were 1.30 mm, 1.72 mm, and 4.62 mm for North, East and Up components, and the reductions with respect to those of the original time series were 18.23%, 15.42%, and 18.25%, respectively. If CMEs were removed from the traditional PCA approach, the corresponding average errors were 1.34 mm, 1.81 mm, and 4.84 mm, with reductions of 15.84%, 10.86%, and 14.32%, respectively. Compared to the traditional PCA approach, the average errors of our approach were reduced by about 2.39%, 4.56%, and 3.93% in the North, East and Up components, respectively. Analysis of CME indicated that it mainly contained white and flicker noise. In the synthetic position time series with 500 repeated simulations, the CME extracted by our approach was closer to the true simulated values than those extracted by the traditional PCA approach, regardless of whether local effects were considered or not. Specifically, the mean root mean square (RMS) reduction of our approach, relative to PCA, ranged from 1.35% to 3.93%. Our simulations illustrated that the RMS reductions depended not only on the magnitude, but also the variation of the formal error series, which further highlights the necessity of considering formal errors in spatiotemporal filtering.

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Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Real Gnss Position Time Series Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Shen

2018

Remote Sensing

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“…Forootan andKusche (2012, 2013) argue that different physical processes generate statistically independent source signals that are superimposed in geophysical time series; thus, application of ICA likely helps separating (extracting) their contribution from the total signal. Therefore, in the recent studies (e.g., Awange et al 2014;Boergens et al 2014;Gualandi et al 2016;Ming et al 2016), ICA has been preferred over the ordinary extensions of the PCA/EOF approach, such as the rotated EOF (REOF) technique applied in, e.g., Richman (1986) and Lian and Chen (2012).…”

Section: Introductionmentioning

confidence: 99%

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

et al. 2017

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In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of realvalued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an

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Filtering of GPS Time Series Using Geophysical Models and Common Mode Error Analysis

Montillet

Bos

et al. 2019

Geodetic Time Series Analysis in Earth Sciences

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Spatiotemporal filtering for regional GPS network in China using independent component analysis

Cited by 53 publications

References 53 publications

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

Filtering of GPS Time Series Using Geophysical Models and Common Mode Error Analysis

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