To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inf...To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inferred from GNSS data for Chinese mainland over two decades were rigorously estimated.First,by analyzing the vertical displacement time series from continuous GNSS stations and environmental load data,we found that the annual and semi-annual vertical displacements are highly correlated.This indicates that the vertical seasonal variations on the ground surface are mainly caused by environmental loading.After removing the seasonal variations caused by environmental loads from the GNSS time series,we applied an improved PCA technique to filter out common mode errors.Next,we estimated the optimal noise models for the filtered time series and derived the vertical velocity field of Chinese mainland.Finally,we employed an empirical Spatial Structure Function(SSF)to image the tectonic deformation of Chinese mainland.This method effectively mitigates issues with abrupt circular arc-shaped boundaries in GNSS imaging caused by sparse station networks.The imaging results show that vertical crustal deformation in Chinese mainland generally ranges from-3 to 3 mm/yr,with significant spatial variability.The central and northern parts of Qinghai-Xizang Plateau are identified as primary subsidence zones,indicating that plate boundaries and tectonic compression continue to shape the crustal movement in these regions.The major uplift zones are located in northern and central China,likely linked to regional tectonic activity and plate compression.Subsidence deformation in parts of eastern China appears to be influenced by human activities.展开更多
基金National Natural Science Foundation of China(42274012,42004001)the Science and Technology Innovation Project of Anhui Surveying and Mapping Bureau(2025-KJ-08)+1 种基金the Open Fund of Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station(WHYWZ202107)the Fundamental Research Funds for the Central Universities(JZ2022HGTB0268)。
文摘To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inferred from GNSS data for Chinese mainland over two decades were rigorously estimated.First,by analyzing the vertical displacement time series from continuous GNSS stations and environmental load data,we found that the annual and semi-annual vertical displacements are highly correlated.This indicates that the vertical seasonal variations on the ground surface are mainly caused by environmental loading.After removing the seasonal variations caused by environmental loads from the GNSS time series,we applied an improved PCA technique to filter out common mode errors.Next,we estimated the optimal noise models for the filtered time series and derived the vertical velocity field of Chinese mainland.Finally,we employed an empirical Spatial Structure Function(SSF)to image the tectonic deformation of Chinese mainland.This method effectively mitigates issues with abrupt circular arc-shaped boundaries in GNSS imaging caused by sparse station networks.The imaging results show that vertical crustal deformation in Chinese mainland generally ranges from-3 to 3 mm/yr,with significant spatial variability.The central and northern parts of Qinghai-Xizang Plateau are identified as primary subsidence zones,indicating that plate boundaries and tectonic compression continue to shape the crustal movement in these regions.The major uplift zones are located in northern and central China,likely linked to regional tectonic activity and plate compression.Subsidence deformation in parts of eastern China appears to be influenced by human activities.
