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
The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum...The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.展开更多
Accurate position measurements are extremely valuable in the shipping industry for various reasons such as safety(collision avoidance),security(situational awareness),fuel-saving(weather identification),punctuality(ro...Accurate position measurements are extremely valuable in the shipping industry for various reasons such as safety(collision avoidance),security(situational awareness),fuel-saving(weather identification),punctuality(route prediction),etc.Although GNSS(Global Navigation Satellite System)receivers installed on-board the ships are proven to be highly accurate,the data logging process may occasionally be problematic,mainly due to the complexity of the measurements and the decimal precision that is required.Data were collected from 3 years of operations of 228 Maersk Line container vessels and an analysis reveals that there is a substantial amount (≈20%) of historical position measurements sent to shore that does not reflect reality.In the study,the sources of the faulty logged position measurements are categorized and an interpolation methodology is proposed to validate and correct them by using AIS(Automatic Identification System)data.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.
基金supported by the National Natural Science Foundation of China(NO.42104028,42174030 and 42004017)the Open Fund of Hubei Luojia Laboratory(No.220100048 and 230100021)the Scientific Research Project of Hubei Provincial Department of Education,and Research Foundation of the Department of Natural Resources of Hunan Province(No.20230104CH)。
文摘The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.
基金supported by“InnovationsFonden Danmark”with case number 8053-00231B and“Den Danske Maritime Fond”with case number 2018-060.
文摘Accurate position measurements are extremely valuable in the shipping industry for various reasons such as safety(collision avoidance),security(situational awareness),fuel-saving(weather identification),punctuality(route prediction),etc.Although GNSS(Global Navigation Satellite System)receivers installed on-board the ships are proven to be highly accurate,the data logging process may occasionally be problematic,mainly due to the complexity of the measurements and the decimal precision that is required.Data were collected from 3 years of operations of 228 Maersk Line container vessels and an analysis reveals that there is a substantial amount (≈20%) of historical position measurements sent to shore that does not reflect reality.In the study,the sources of the faulty logged position measurements are categorized and an interpolation methodology is proposed to validate and correct them by using AIS(Automatic Identification System)data.
