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<p>Common mode errors (CMEs) are a persistent challenge in regional Global Navigation Satellite System (GNSS) coordinate time series, becoming more difficult to model or separate as the spatial scale of the network increases. This study applies an adaptive spatiotemporal filtering method that divides the network into sub-regions using data-driven distance and correlation thresholds. A total of 208 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) were analyzed, with data from 2013 to mid-2023 in the North (N), East (E), and Up (U) directions. Three filtering schemes were compared: global Principal Component Analysis (PCA) spatiotemporal filtering, active tectonic block region filtering, and adaptive sub-regional filtering. The results demonstrate that the adaptive spatiotemporal filtering method reduced the root mean square (RMS) values in the N, E, and U directions by an average of 23.38%, 19.61%, and 29.45%, respectively, compared to unfiltered data. The active tectonic block region spatiotemporal filtering achieved reductions of 21.39%, 20.59%, and 29.15% in the RMS values for the same directions. Both methods outperformed the global PCA spatiotemporal filtering, which resulted in RMS reductions of 15.92%, 15.20%, and 17.93% for the N, E, and U directions, respectively. Assessment of surface mass loading effects on GNSS Common Mode Errors (CMEs) shows that, after mass loading correction, the adaptive sub-regional filtering reduces the amplitude of seasonal variations in the vertical (U) CMEs by 43%, significantly outperforming the global PCA approach, which achieves a reduction of 23%. While no consistent improvement was observed in the N component, the adaptive approach more effectively isolates vertical deformation signals consistent with environmental loading. An amplitude increase was noted in the E direction. Noise analysis indicated significant enhancements, with adaptive sub-regional filtering further reducing power law noise (PL) by 16.02%, 8.82%, and 15.45% in N, E, and U directions, respectively, compared to global PCA filtering. This study confirms that the proposed adaptive framework more faithfully captures the spatial variability of tectonic and geophysical signals, offering a robust and objective pathway for high-precision GNSS time series analysis.</p> Graphical Abstract <p></p>

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An adaptive spatiotemporal filtering method for GNSS coordinate time series of CMONOC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Bin Liu,
  • Yifan Jiang,
  • Zien Xiao,
  • Weilong Rao,
  • Tengfei Feng

摘要

Common mode errors (CMEs) are a persistent challenge in regional Global Navigation Satellite System (GNSS) coordinate time series, becoming more difficult to model or separate as the spatial scale of the network increases. This study applies an adaptive spatiotemporal filtering method that divides the network into sub-regions using data-driven distance and correlation thresholds. A total of 208 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) were analyzed, with data from 2013 to mid-2023 in the North (N), East (E), and Up (U) directions. Three filtering schemes were compared: global Principal Component Analysis (PCA) spatiotemporal filtering, active tectonic block region filtering, and adaptive sub-regional filtering. The results demonstrate that the adaptive spatiotemporal filtering method reduced the root mean square (RMS) values in the N, E, and U directions by an average of 23.38%, 19.61%, and 29.45%, respectively, compared to unfiltered data. The active tectonic block region spatiotemporal filtering achieved reductions of 21.39%, 20.59%, and 29.15% in the RMS values for the same directions. Both methods outperformed the global PCA spatiotemporal filtering, which resulted in RMS reductions of 15.92%, 15.20%, and 17.93% for the N, E, and U directions, respectively. Assessment of surface mass loading effects on GNSS Common Mode Errors (CMEs) shows that, after mass loading correction, the adaptive sub-regional filtering reduces the amplitude of seasonal variations in the vertical (U) CMEs by 43%, significantly outperforming the global PCA approach, which achieves a reduction of 23%. While no consistent improvement was observed in the N component, the adaptive approach more effectively isolates vertical deformation signals consistent with environmental loading. An amplitude increase was noted in the E direction. Noise analysis indicated significant enhancements, with adaptive sub-regional filtering further reducing power law noise (PL) by 16.02%, 8.82%, and 15.45% in N, E, and U directions, respectively, compared to global PCA filtering. This study confirms that the proposed adaptive framework more faithfully captures the spatial variability of tectonic and geophysical signals, offering a robust and objective pathway for high-precision GNSS time series analysis.

Graphical Abstract