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.

Helmert transformation strategies in analysis of GPS position time-series

2020 ◽  
Vol 223 (2) ◽  
pp. 973-992
Author(s):  
Shiwei Guo ◽  
Chuang Shi ◽  
Na Wei ◽  
Min Li ◽  
Lei Fan ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Frame ◽  
Scale Factor ◽  
Network Effect ◽  
Satellite Orbits ◽  
Surface Mass ◽  
Helmert Transformation ◽  
Position Time Series ◽  
Transformation Parameters ◽  
The Impact

SUMMARY Global positioning system (GPS) position time-series generated using inconsistent satellite products should be aligned to a secular Terrestrial Reference Frame by Helmert transformation. However, unmodelled non-linear variations in station positions can alias into transformation parameters. Based on 17 yr of position time-series of 112 stations produced by precise point positioning (PPP), we investigated the impact of network configuration and scale factor on long-term time-series processing. Relative to the uniform network, the uneven network can introduce a discrepancy of 0.7–1.1 mm, 21.3–27.5 μas and 1.3 mm in terms of root mean square (RMS) for the translation, rotation and scale factor (if estimated), respectively, no matter whether the scale factor is estimated. The RMS of vertical annual amplitude differences caused by such network effect reaches 0.5–0.6 mm. Whether estimating the scale factor mostly affects the Z-translation and vertical annual amplitude, leading to a difference of 1.3 mm when the uneven network is used. Meanwhile, the annual amplitude differences caused by the scale factor present different geographic location dependences over the north, east and up components. The seasonal signals derived from the transformation using the uniform network and without estimating scale factor have better consistency with surface mass loadings with more than 41 per cent of the vertical annual variations explained. Simulation studies show that 40–50 per cent of the annual signals in the scale factor can be explained by the aliasing of surface mass loadings. Another finding is that GPS draconitic errors in station positions can also alias into transformation parameters, while different transformation strategies have limited influence on identifying the draconitic errors. We suggest that the uniform network should be used and the scale factor should not be estimated in Helmert transformation. It is also suggested to perform frame alignment on PPP time-series, even though the used satellite products belong to a consistent reference frame, as the origin of PPP positions inherited from satellite orbits and clocks is not so stable during a long period. With Helmert transformation, the seasonal variations would better agree with surface mass loadings, and noise level of time-series is reduced.

Station Classification and Reference Station Selection

2021 ◽  
Author(s):  
Juliette Legrand ◽  
Carine Bruyninx
Keyword(s):  
Reference Frame ◽  
Reference Station ◽  
Web Tool ◽  
Additional Information ◽  
Position Time Series ◽  
Information Number ◽  
Seismic Deformation ◽  
Work Done ◽  
Gnss Processing ◽  
Selection Of

<p>When using a network approach, expressing reliably GNSS position and velocities in a given reference frame (ITRF2014, IGS14, …) requires the identification of ‘stable’ and ‘rebliable’ reference stations. The choice of these reference stations can have a non-negligible impact on the estimated positions and velocities and of course on the derived geodynamic interpretations.</p><p>This study will present the work done to address this issue within EUREF and help the users of the EUREF products (more specifically of the EPN multi-year position and velocity solution) to identify the best reference stations in the EUREF Permanent Network (EPN). To that aim, in addition to a station classification, a web tool (https://epncb.oma.be/_productsservices/ReferenceFrame/) has been developed in order to discover the most suitable EPN reference stations. The primary goal of this tool is to help the user of EUREF reference frame product select suitable EPN reference stations to be added to his network during the preparation of own GNSS processing.</p><p>The tool helps the selection of optimal reference stations:</p><ul><li>by providing a restricted list of reference stations (based on the station classification and the begin and end date of the user processing)</li> <li>by giving access to additional information (number of position or velocity discontinuities, post-seismic deformation,…) and plots (detrended position time series, selection criteria values, velocity variability) for the stations.</li> </ul><p>The web tool as well as the station classification will be presented.</p>

scholarly journals Verification of the Polish Geodetic Reference Frame by Means of a New Solution Based on Permanent GNSS Data from the Years 2011-2014

2016 ◽  
Vol 102 (1) ◽  
pp. 52-66 ◽  
Author(s):  
T. Liwosz ◽  
M. Ryczywolski
Keyword(s):  
Reference Frame ◽  
Primary Network ◽  
Position Time Series ◽  
Gnss Network ◽  
Gnss Data ◽  
Geodetic Reference Frame ◽  
Minimum Constraints ◽  
Data Length ◽  
Good Agreement

Abstract The new solution for the Polish geodetic primary GNSS network was created to verify the currently used reference frame (PL-ETRF2000). The new solution is based on more GNSS data (more daily observation sessions included, a longer data timespan, GLONASS observations added) which were processed in a newer reference frame (IGb08) according to up-to-date methodology and using the latest version of Bernese GNSS Software. The new long-term solution (spanning 3.7 years) was aligned to the IGb08 reference frame using a minimum constraints approach. We categorized Polish reference stations into two categories according to their data length. We obtained good agreement of the new solution with the PL-ETRF2000: for most stations position differences did not exceed 5 mm in horizontal, and 10 mm in vertical components. However, for 30 stations we observed discontinuities in position time series, mostly due to GNSS equipment changes, which occured after the introduction of PL-ETRF2000. Position changes due to the discontinuities reached 9.1 mm in horizontal components, and 26.9 mm in vertical components. The new solution takes into account position discontinuities, and in addition also includes six new stations which were installed after the introduction of the PL-ETRF2000. Therefore, we propose to update the currently-used reference frame for the Polish geodetic primary network (PL-ETRF2000) with the new solution. The new solution was also accepted by the EUREF Technical Working Group as a class A solution (highest accuracy) according to EUREF standards.

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>

Developing a highly-available GNSS reference station network

2020 ◽  
Author(s):  
Ryan Ruddick ◽  
Amy Peterson ◽  
Richard Jacka ◽  
Bart Thomas
Keyword(s):  
Reference Frame ◽  
Satellite System ◽  
Asia Pacific ◽  
Community Safety ◽  
Reference Station ◽  
The Pacific ◽  
Australian Continent ◽  
Global Navigation Satellite ◽  
Geodetic Reference Frame ◽  
Reduced Data

<p>Having modern and well-maintained geodetic infrastructure is critical for the development of an accurate and reliable Global Geodetic Reference Frame (GGRF). Geoscience Australia (GA) contributes to the development of the GGRF through a network of Global Navigation Satellite System (GNSS) reference stations positioned in key locations across Australia, Antarctica and the Pacific. Data from these reference stations contribute to the realisation of the GGRF, the development and maintenance of the Asia-Pacific Reference frame and the monitoring of deformation across the Australian continent. We are also seeing a rapid increase in the use of this data for location-based positioning applications, such as civil engineering, transport, agriculture and community safety. These applications bring with them a new suite of challenges for geodetic infrastructure operators, such as reduced data latency, denser networks and accessing the latest signals in the most modern formats. Through the Positioning Australia program, GA is addressing these challenges by developing a modern highly-available GNSS reference station design that will be deployed at over 200 sites across the region. This paper discusses the concept of highly-available infrastructure and presents a GNSS reference station design that is openly available for use by the global geodetic community.</p>

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 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.

scholarly journals Precision of continuous GPS velocities from statistical analysis of synthetic time series

2018 ◽  
Author(s):  
Christine Masson ◽  
Stephane Mazzotti ◽  
Philippe Vernant
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Regression Tree ◽  
Simple Method ◽  
Position Time Series ◽  
Gps Velocities ◽  
Synthetic Time Series ◽  
Continuous Gps ◽  
Automatic Treatment ◽  
Gps Noise

Abstract. We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, followed by the presence of offsets and the noise (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain the velocity accuracies (expressed as 95 % confidence limits) based on simple parameters: (1) Series durations over 8.0 years result in high velocity accuracies in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1); (2) Series durations of less than 4.5 years cannot be used for high-precision studies since the horizontal accuracy is insufficient (over 1.0 mm yr−1); (3) Series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal accuracy (0.6 mm yr-1) and a poor vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a better accuracy compare to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

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