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
GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this pa...Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.展开更多
In this study we compared weekly GNSS position time series with modelled values of crustal deformations on the basis of Gravity Recovery and Climate Experiment （GRACE） data. The Global Navigation Satellite Systems ...In this study we compared weekly GNSS position time series with modelled values of crustal deformations on the basis of Gravity Recovery and Climate Experiment （GRACE） data. The Global Navigation Satellite Systems （GNSS） time series were taken from homogeneously reprocessed global network solutions within the International GNSS Service （IGS） Reprucessing 1 project and from regional solutions performed by Warsaw University of Technology （WUT） European Permanent Network （EPN） Local Analysis Center （LAC） within the EPN reprocessing project. Eight GNSS sites from the territory of Poland with observation timespans between 2.5 and 13 years were selected for this study. The Total Water Equivalent （TWE） estimation from GRACE data was used to compute deformations using the Green＇s function formalism. High frequency components were removed from GRACE data to avoid aliasing problems. Since GRACE observes mainly the mass transport in continental storage of water, we also compared GRACE deformations and the GNSS position time series, with the deformations computed on the basis of a hydrosphere model. We used the output of Water GAP Hydrology Model （WGHM） to compute deformations in the same manner as for the GRACE data. The WGHM gave slightly larger amplitudes than GNSS and GRACE. The atmospheric non-tidal loading effect was removed from GNSS position time series before comparing them with modelled deformations. The results confirmed that the major part of observed seasonal variations for GNSS vertical components can be attributed to the hy- drosphere loading. The results for these components agree very well both in the amplitude and phase. The decrease in standard deviation of the residual GNSS position time series for vertical components corrected for the hydrosphere loading reached maximally 36% and occurred for all but one stations for both global and regional solutions. For horizontal components the amplitudes are about three times smaller than for vertical components therefore the comparison is much more complicated and the conclusions are ambiguous.展开更多
The velocities of tectonic plates derived from GNSS time series are regularly used as input data for geophysical models. However, as shown by numerous researches, the coordinates time series contain residual errors of...The velocities of tectonic plates derived from GNSS time series are regularly used as input data for geophysical models. However, as shown by numerous researches, the coordinates time series contain residual errors of a systematic nature, which can significantly affect the reliability of the obtained velocity estimates. This research shows that using non-classical error theory of measurement(NETM)for processing GNSS time series allows detecting the presence of weak, not removed from GNSS processing, sources of systematic errors. Based on the coordinate time series of selected permanent GNSS stations in Europe, we checked the empirical distributions of errors by the NETM on G. Jeffries’ recommendations and on the principles of the theory of hypothesis tests according to Pearson’s criterion. It is established that the obtained coordinates time series of GNSS-stations only partially confirm the hypothesis of their conformity to the normal Gaussian distribution law, and this may be the main reason for their unrepresentative classification. In the future, it is necessary to identify and take into account the causes of residual errors that distort the real distribution of the results of the GNSS time series.展开更多
Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned sign...Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.展开更多
With the increasingly high requirement of clock source accuracy for seismic data servers and equipment,the development of a multipurpose timing system is urgently needed in the seismic industry. We have developed low-...With the increasingly high requirement of clock source accuracy for seismic data servers and equipment,the development of a multipurpose timing system is urgently needed in the seismic industry. We have developed low-cost timing equipment according to the actual earthquake industry situation. This set of timing equipment can provide a unified solution to the different environment and different earthquake instruments with different timing precision demands.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
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(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Program(JD)of Hubei Province(Grant No.2023BAA026)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.
文摘In this study we compared weekly GNSS position time series with modelled values of crustal deformations on the basis of Gravity Recovery and Climate Experiment （GRACE） data. The Global Navigation Satellite Systems （GNSS） time series were taken from homogeneously reprocessed global network solutions within the International GNSS Service （IGS） Reprucessing 1 project and from regional solutions performed by Warsaw University of Technology （WUT） European Permanent Network （EPN） Local Analysis Center （LAC） within the EPN reprocessing project. Eight GNSS sites from the territory of Poland with observation timespans between 2.5 and 13 years were selected for this study. The Total Water Equivalent （TWE） estimation from GRACE data was used to compute deformations using the Green＇s function formalism. High frequency components were removed from GRACE data to avoid aliasing problems. Since GRACE observes mainly the mass transport in continental storage of water, we also compared GRACE deformations and the GNSS position time series, with the deformations computed on the basis of a hydrosphere model. We used the output of Water GAP Hydrology Model （WGHM） to compute deformations in the same manner as for the GRACE data. The WGHM gave slightly larger amplitudes than GNSS and GRACE. The atmospheric non-tidal loading effect was removed from GNSS position time series before comparing them with modelled deformations. The results confirmed that the major part of observed seasonal variations for GNSS vertical components can be attributed to the hy- drosphere loading. The results for these components agree very well both in the amplitude and phase. The decrease in standard deviation of the residual GNSS position time series for vertical components corrected for the hydrosphere loading reached maximally 36% and occurred for all but one stations for both global and regional solutions. For horizontal components the amplitudes are about three times smaller than for vertical components therefore the comparison is much more complicated and the conclusions are ambiguous.
文摘The velocities of tectonic plates derived from GNSS time series are regularly used as input data for geophysical models. However, as shown by numerous researches, the coordinates time series contain residual errors of a systematic nature, which can significantly affect the reliability of the obtained velocity estimates. This research shows that using non-classical error theory of measurement(NETM)for processing GNSS time series allows detecting the presence of weak, not removed from GNSS processing, sources of systematic errors. Based on the coordinate time series of selected permanent GNSS stations in Europe, we checked the empirical distributions of errors by the NETM on G. Jeffries’ recommendations and on the principles of the theory of hypothesis tests according to Pearson’s criterion. It is established that the obtained coordinates time series of GNSS-stations only partially confirm the hypothesis of their conformity to the normal Gaussian distribution law, and this may be the main reason for their unrepresentative classification. In the future, it is necessary to identify and take into account the causes of residual errors that distort the real distribution of the results of the GNSS time series.
基金supported by the National Natural Science Foundation of China(Grant No.42192535,42174012,42174101,41974023)the Open Fund of Hubei Luojia Laboratory(Grant No.S22H640201)。
文摘Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.
基金supported by the Xinjiang Earthquake Science Foundation(201308)
文摘With the increasingly high requirement of clock source accuracy for seismic data servers and equipment,the development of a multipurpose timing system is urgently needed in the seismic industry. We have developed low-cost timing equipment according to the actual earthquake industry situation. This set of timing equipment can provide a unified solution to the different environment and different earthquake instruments with different timing precision demands.
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.
