Assessment of spatiotemporal filtering methods towards optimising crustal movement observation network of China (CMONOC) GNSS data processing at different spatial scales

被引:3
作者
Wang, Hao [1 ]
Li, Wenhao [1 ]
Shu, Chanfang [1 ]
Shum, C. K. [2 ]
Li, Fei [3 ]
Zhang, Shengkai [4 ]
Zhang, Zikang [1 ]
机构
[1] Nanjing Tech Univ, Sch Geomat Sci & Technol, Nanjing, Peoples R China
[2] Ohio State Univ, Sch Earth Sci, Div Geodet Sci, Columbus, OH USA
[3] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China
[4] Wuhan Univ, Chinese Antarct Ctr Surveying & Mapping, Wuhan, Peoples R China
来源
ALL EARTH | 2022年 / 34卷 / 01期
基金
中国国家自然科学基金;
关键词
GNSS; common mode error; Correlation-weighted spatial filtering; INDEPENDENT COMPONENT ANALYSIS; COORDINATE TIME-SERIES; GPS; ALGORITHMS; STATIONS; NOISE;
D O I
10.1080/27669645.2022.2098611
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Spatiotemporal filtering can effectively remove the common mode error (CME) which significantly affects the accuracy of the Global Navigation Satellite System (GNSS) coordinate time series. This contribution explores the performance of different spatiotemporal filtering methods applied to GNSS networks at different spatial scales. We selected small-scale (<500 km) and large-scale (>2000 km) GNSS networks from the Crustal Movement Observation Network of China (CMONOC) for the focus of the study. To remove or mitigate CME from the different-scale GNSS networks, principal component analysis (PCA), independent component analysis (ICA) and correlation-weighted spatial filtering (CWSF) are compared. In addition, we investigate the correlations between each of the GNSS station residual time series to examine the effectiveness of the novel CME filter. When compared with PCA and ICA results, we find that CWSF is less intrusive on the data and is more effective in reducing the CME in the different-scale GNSS networks, and thus the preferred the filtering methodology. We conclude that this study could provide an important reference to remove CME from GNSS coordinate time series.
引用
收藏
页码:107 / 119
页数:13
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