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.
