Preparatory analysis and development for the ITRF2020

2021 ◽  
Author(s):  
Zuheir Altamimi ◽  
Paul Rebischung ◽  
Laurent Metivier ◽  
Xavier Collilieux ◽  
Kristel Chanard ◽  
...  
Keyword(s):  
Time Series ◽  
Input Data ◽  
Parametric Models ◽  
Laser Ranging ◽  
Early Results ◽  
Analysis Strategies ◽  
Position Time Series ◽  
Periodic Signals ◽  
Deformation Models ◽  
Seismic Deformation

<p>In preparation for ITRF2020, we developed a number of software tools and analysis strategies aiming at improving the quality, consistency and accuracy of the new frame. Our target is to enhance the modelling of the nonlinear station motions, including post-seismic deformation models for stations subject to major earthquakes, and periodic signals embedded in the station position time series. In addition to the classical annual and semi-annual signals, we foresee to simultaneously adjust some satellite draconitic harmonics and evaluate their impact on the estimated frame parameters.  The ITRF2020 is expected to be provided in the form of an augmented reference frame so that in addition to station positions and velocities, parametric models for both PSD and periodic signals (expressed in the CM frame of satellite laser ranging) will also be delivered to the users. Depending on the availability of the input data of the four techniques at the time of this presentation, we expect to show and discuss some early results and give some indications regarding the specifications of the final ITRF2020 solution.</p>

scholarly journals General Geometric Model of GNSS Position Time Series for Crustal Deformation Studies – A Case Study of CORS Stations in Vietnam

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dinh Trong TRAN ◽  
Quoc Long NGUYEN ◽  
Dinh Huy NGUYEN
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Linear Trend ◽  
Geometric Model ◽  
Real Data ◽  
Deformation Monitoring ◽  
Calculation Results ◽  
Position Time Series ◽  
Seismic Deformation ◽  
Gnss Position Time Series

In processing of position time series of crustal deformation monitoring stations by continuousGNSS station, it is very important to determine the motion model to accurately determine the displacementvelocity and other movements in the time series. This paper proposes (1) the general geometric model foranalyzing GNSS position time series, including common phenomena such as linear trend, seasonal term,jumps, and post-seismic deformation; and (2) the approach for directly estimating time decay ofpostseismic deformations from GNSS position time series, which normally is determined based on seismicmodels or the physical process seismicity, etc. This model and approach are tested by synthetic positiontime series, of which the calculation results show that the estimated parameters are equal to the givenparameters. In addition they were also used to process the real data which is GNSS position time series of4 CORS stations in Vietnam, then the estimated velocity of these stations: DANA (n, e, u = -9.5, 31.5, 1.5mm/year), HCMC (n, e, u = -9.5, 26.2, 1.9 mm/year), NADI (n, e, u = -10.6, 31.5, -13.4 mm/year), andNAVI (n, e, u = -13.9, 32.8, -1.1 mm/year) is similar to previous studies.

scholarly journals Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China

2018 ◽  
Vol 10 (9) ◽  
pp. 1472 ◽  
Author(s):  
Peng Yuan ◽  
Weiping Jiang ◽  
Kaihua Wang ◽  
Nico Sneeuw
Keyword(s):  
Time Series ◽  
Velocity Field ◽  
Colored Noise ◽  
Environmental Loading ◽  
Movement Observation ◽  
Observation Network ◽  
Uncertainty Estimates ◽  
Position Time Series ◽  
Periodic Signals ◽  
Gps Velocity Field

Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental loading effects, and the estimation of a realistic and unbiased GPS velocity field for geodynamic applications. In this paper, we homogeneously processed the daily observations of 231 Crustal Movement Observation Network of China (CMONOC) Continuous GPS stations to obtain their position time series. Then, we filtered out the CMC and evaluated its effects on the periodic signals and noise for the CMONOC time series. Results show that, with CMC filtering, peaks in the stacked power spectra can be reduced at draconitic harmonics up to the 14th, supporting the point that the draconitic signal is spatially correlated. With the colored noise suppressed by CMC filtering, the velocity uncertainty estimates for both of the two subnetworks, CMONOC-I (≈16.5 years) and CMONOC-II (≈4.6 years), are reduced significantly. However, the CMONOC-II stations obtain greater reduction ratios in velocity uncertainty estimates with average values of 33%, 38%, and 54% for the north, east, and up components. These results indicate that CMC filtering can suppress the colored noise amplitudes and improve the precision of velocity estimates. Therefore, a unified, realistic, and three-dimensional CMONOC GPS velocity field estimated with the consideration of colored noise is given. Furthermore, contributions of environmental loading to the vertical CMC are also investigated and discussed. We find that the vertical CMC are reduced at 224 of the 231 CMONOC stations and 170 of them are with a root mean square (RMS) reduction ratio of CMC larger than 10%, confirming that environmental loading is one of the sources of CMC for the CMONOC height time series.

scholarly journals Semi-kinematic geodetic reference frame based on the ITRF2014 for Malaysia

2020 ◽  
Vol 10 (1) ◽  
pp. 91-109
Author(s):  
M. Azhari ◽  
Z. Altamimi ◽  
G. Azman ◽  
M. Kadir ◽  
W.J.F Simons ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Frame ◽  
Spatial Databases ◽  
Parametric Models ◽  
Plate Motion ◽  
Reference Station ◽  
Gis Mapping ◽  
Tectonically Active ◽  
Position Time Series ◽  
Geodetic Reference Frame

Abstract Malaysia is located at the stable part of the tec-tonic Sundaland platelet in SE Asia. The platelet is surrounded in almost every direction by tectonically active convergent boundaries, at which the Philippine Sea, the Australian and the Indian Plates are subducting respectively from the East, South and West.The current Malaysia geodetic reference frame called MGRF2000 is a static reference frame and hence did not incorporate the effects of plate motion and the ensuing deformation from (megath-rust) earthquakes. To prevent degradation of Continuously Operating Reference Station (CORS) coordinates, a new time-dependent national reference frame was developed. Taking advantage of the availability of the GNSS data of the CORS network in Malaysia, notably the Malaysia Active GPS System (MASS) and Malaysia Real-Time Kinematic GNSS Network (MyRTKnet), a more accurate and robust Malaysian geodetic reference frame was determined, fully aligned and compatible with ITRF2014. The cumulative solution obtained from stacking Malaysian CORS position time series formed the basis of the new MGRF2020 realization. It consists of 100+ station positions at epoch 2020.0, station velocities and Post-Seismic Deformation (PSD) parametric models for stations subjected to major earthquakes. The (1999-2018) position time series exhibit Weighted Mean Root Square (WRMS) values of 3.0, 3.2 and 7.6 mm in respectively the East, North and Vertical components. A new semi-kinematic geodetic datum (GDM2020) for Malaysia, useable for GIS, mapping and cadastre applications is proposed to replace the existing static datum (GDM2000). A transformation suite to convert the spatial databases from GDM2000 to GDM2020 was also developed.

scholarly journals Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Jun Hu ◽  
Qiaoqiao Ge ◽  
Jihong Liu ◽  
Wenyan Yang ◽  
Zhigui Du ◽  
...  
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Surface Deformation ◽  
A Priori ◽  
Least Square ◽  
Deformation Model ◽  
Phase Time ◽  
Testing Theory ◽  
Sbas Insar ◽  
Deformation Models

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

scholarly journals GPS measurements on pre-, co- and post-seismic surface deformation at first multi-parametric geophysical observatory, Ghuttu in Garhwal Himalaya, India

2020 ◽  
Vol 10 (1) ◽  
pp. 136-144
Author(s):  
P.K. Gautam ◽  
S. Rajesh ◽  
N. Kumar ◽  
C.P. Dabral
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Translational Motion ◽  
Anomalous Behavior ◽  
Deformation Pattern ◽  
Local Earthquakes ◽  
Position Time Series ◽  
Continuous Gps ◽  
Gps Time Series ◽  
Gps Station

Abstract We investigate the surface deformation pattern of GPS station at MPGO Ghuttu (GHUT) to find out the cause of anomalous behavior in the continuous GPS time series. Seven years (2007-2013) of GPS data has been analyzed using GAMIT/GLOBK software and generated the daily position time series. The horizontal translational motion at GHUT is 43.7 ± 1 mm/yr at an angle of 41°± 3° towards NE, while for the IGS station at LHAZ, the motion is 49.4 ±1 mm/yr at 18 ± 2.5° towards NEE. The estimated velocity at GHUT station with respect to IISC is 12 ± 1 mm/yr towards SW. Besides, we have also examined anomalous changes in the time series of GHUT before, after and during the occurrences of local earthquakes by considering the empirical strain radius; such that, a possible relationship between the strain radius and the occurrences of earthquakes have been explored. We considered seven local earthquakes on the basis of Dobrovolsky strain radius condition having magnitude from 4.5 to 5.7, which occurred from 2007 to 2011. Results show irrespective of the station strain radius, pre-seismic surface deformational anomalies are observed roughly 70 to 80 days before the occurrence of a Moderate or higher magnitude events. This has been observed for the cases of those events originated from the Uttarakashi and the Chamoli seismic zones in the Garhwal and Kumaun Himalaya. Occurrences of short (< 100 days) and long (two years) inter-seismic events in the Garhwal region plausibly regulating and diffusing the regional strain accumulation.

scholarly journals Multiscale Post-Seismic Deformation Based on cGNSS Time Series Following the 2015 Lefkas (W. Greece) Mw6.5 Earthquake

2021 ◽  
Vol 11 (11) ◽  
pp. 4817
Author(s):  
Filippos Vallianatos ◽  
Vassilis Sakkas
Keyword(s):  
Time Series ◽  
Relaxation Time ◽  
Relaxation Times ◽  
Relaxation Mechanism ◽  
Mesoscopic Scale ◽  
Macroscopic Scale ◽  
Epicentral Area ◽  
Arrhenius Dependence ◽  
Seismic Deformation ◽  
Logarithmic Type

In the present work, a multiscale post-seismic relaxation mechanism, based on the existence of a distribution in relaxation time, is presented. Assuming an Arrhenius dependence of the relaxation time with uniform distributed activation energy in a mesoscopic scale, a generic logarithmic-type relaxation in a macroscopic scale results. The model was applied in the case of the strong 2015 Lefkas Mw6.5 (W. Greece) earthquake, where continuous GNSS (cGNSS) time series were recorded in a station located in the near vicinity of the epicentral area. The application of the present approach to the Lefkas event fits the observed displacements implied by a distribution of relaxation times in the range τmin ≈3.5 days to τmax ≈350 days.

scholarly journals On the Connection between the GEP Performances and the Time Series Properties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1853
Author(s):  
Alina Bărbulescu ◽  
Cristian Ștefan Dumitriu
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Financial Time Series ◽  
Gene Expression Programming ◽  
Parametric Models ◽  
Statistical Characteristics ◽  
Data Series ◽  
Financial Time ◽  
Data Generating Process

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

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