Common Mode Component and Its Potential Effect on GPS-Inferred Crustal Deformations in Greenland

Bian, Yankai; Yue, Jianping; Ferreira, Vagner G.; Cong, Kanglin; Cai, Dongjian

doi:10.1007/s00024-021-02732-z

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…PCA is a common method for reducing the dimensionality of high-dimensional data. The principle is to transform the original data into a set of linearly independent components, where the first few components with high contribution are used to represent the original data [19].…”

Section: Principal Component Analysismentioning

confidence: 99%

“…CME affect the accuracy of station coordinates and velocity solutions [17] and constitute one of the largest sources of the errors in the accuracy of regional network time series [18]. CME can be efficiently extracted by methods such as spatial filtering, mainly stacking, principal component analysis (PCA) and Karhunen-Loeve expansion (KLE) methods [19,20]. Among them, PCA filtering shows the best performance, making the station coordinates more convergent and effectively reducing the uncertainty of station coordinates [21].…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

Zhang,

Wang

et al. 2024

Applied Sciences

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The motion of a continuously operating reference station is usually dominated by the long-term crustal motions of the tectonic block on which the station is located. Monitoring changes in the coordinates of reference stations located at tectonic plate boundaries allows for the calculation of velocity fields that reflect the spatial and temporal characteristics of the region. This study analyzes the spatiotemporal relationships of regional reference frame points with GNSS data from 25 reference stations in Sichuan, China, from 2015 to 2021. The common mode errors are extracted and eliminated by principal component analysis. A time series function model is developed for the reference stations and their constituent baselines for calculating the velocity field. Subsequently, the spatiotemporal characteristics of the regional reference frame in Sichuan is analyzed by a stochastic model. The results show that the influences of the common mode error on the horizontal and vertical directions of the reference stations is 2.5 mm and 4.3 mm, respectively. Generally, the horizontal motion of the reference stations in the Sichuan region tends to be in the southeast direction and the vertical motion trend is mainly uplifting. The east–west and vertical components of the baseline tend to be shortened, and the random influence among the reference stations is larger in the north–south and east–west directions—0.39 mm and 0.54 mm, respectively. Polynomial functions are more appropriate for constructing the fitted random influence covariance model.

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Section: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen,

Zhang,

Wang

et al. 2024

Applied Sciences

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show abstract

“…These mismodeled effects in continuous GPS time series can be well described as colored noise [33,34]. The effective of the removal and destruction of CMEs plays a vital role in further enhancing the correctness of GPS data analysis [35].…”

Section: Common Mode Errorsmentioning

confidence: 99%

Strain Field Features and Three-Dimensional Crustal Deformations Constrained by Dense GRACE and GPS Measurements in NE Tibet

Zhang

Shen

et al. 2022

Remote Sensing

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The continuing impact between the Eurasia Plate and India results in the thickening and shortening of the N-S Tibetan Plateau. There has been strong tectonic movement along the boundary of the zones of deformation of the NE corner of the Tibetan plateau (NET) since the new tectonic period, with its dynamic mechanisms remaining controversial. Here, we use observations of 39 Continuous Global Positioning System (CGPS) gauges and 451 Crustal Movement Observation Network of China (CMONOC) campaign-mode stations to detect the three-dimensional deformation of the crust in the NET. Improved processing procedures are implemented to strengthen the patterns of strain throughout the NET. The principal component analysis (PCA) technique is introduced to decompose the time series into spatial eigenvectors and principal components (PCs), and the first three PCs are used to estimate and rectify common mode errors (CMEs). In addition, GRACE observations are used to detect deformation changes that account for non-tidal oceanic mass loading, hydrological loading, and surface pressure. The rectified deformation of the crust indicates the anisotropic nature of both the subsidence and uplift, and that the highest uplift rate of the Longmen Shan fault uplift reaches 7.13 ± 0.53 mm/yr. Finally, the horizontal velocity is further used to enumerate the strain rates throughout the NET. The results show that the shear band retained property in line with the strike-slip fault along the Altyn Tagh fault, the Qilian Shan faults, the Haiyuan fault, the West Qinling fault, the East Kunlun fault, and the Longmen Shan fault. In addition, the results further indicate that the whole NET shows a strong relationship with the mean principal rates of horizontal shortening strain. Extension and compression of the crust reasonably describe its sinking and uplifting.

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Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning

Xu,

Hu,

Jin

et al. 2024

GPS Solut

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Common Mode Component and Its Potential Effect on GPS-Inferred Crustal Deformations in Greenland

Cited by 8 publications

References 43 publications

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

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

Strain Field Features and Three-Dimensional Crustal Deformations Constrained by Dense GRACE and GPS Measurements in NE Tibet

Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning

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