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Introduction

The rapid development in the accuracy achieved by GNSS along with other space geodetic techniques such as SLR, VLBI has made it possible to accurately determine regional and global reference frames.This precise realization of the reference system is only possible through precise determination of 3D coordinates of fixed markers on the Earth‘s surface (geodetic observing stations). The long term stable, precise and regularly updated reference frames provides well understanding of physical processes (eg. ice melting, sea level rise, tectonic movement etc.) undergoing in earth system and its temporal changes.Precise GNSS data processing plays a crucial steps for time series analysis of permanent GNSS station which leads to determination of the station’s velocity.

The regular and irregular physical process going in earth such as seasonal changes, tectonic movement, ice melting, sea level rise etc. has direct and indirect effect on the permanently fixed markers (stations) on Earth surface which changes the magnitude and velocity of such markers. The coordinated time series provides linear/non-linear, annual/semi-annual, continuity/discontinuity trend of such stations which helps in better understanding of such geophysical phenomena. This changes in coordinated time series is also possible due to technical/mechanical error such as hardware/software failure, and local disturbances in antennae which is necessary to be addressed beforehand.

Data used

Use of UNAVCO website (https://www.unavco.org/data/gps-gnss/data-access-methods/dai1/perm_sta.php) was done to select and download GNSS data for each permanent station around the project area. Not all available stations could be used. Filtering was done for retired stations and stations with operating periods shorter than two years. Metadata (log files) and daily GNSS data (RINEX files) for each station from 2010 to 2019 were downloaded. The downloaded data for each station for each year was checked to detect data gaps that could seriously affect the results. There exists many new stations which did not have observation before 2016. The stations having larger data gaps were avoided and the stations for which there is at least two years of continuous observations were selected.

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The log files for each station were used to construct the station information file (*.STA) required by the Bernese Software for the data processing. Change in receiver type or its firmware version was noticed for maximum number of stations. The observation epoch for each interval when the changes occurred were listed separately.

Preliminary station coordinates

The observation files comprised of Hatanaka format (downloaded from UNAVCO website) were converted to RINEX format using application CRX2RNX.COM. This application uncompressed the compressed Hatanaka format and convert the files with *.yyD extension to *.yyO. extension. These observations files are later used in processing.

In order to compute preliminary coordinates for the stations, the RINEX files of the stations on an arbitrarily selected day (in this case day 100, 2019) were chosen. With this RINEX, the preliminary station coordinates were computed at AUSPOS web service (http://www.ga.gov.au/bin/gps.pl), where the observation file of each station of GPS day 100 was uploaded with information on antennae height and antennae type. The results of computed coordinates were collected from the provided email address.

GNSS Pre-processing for Bernese software

The input files for Bernese software were prepared. For GNSS processing the following directories are to be created inside Campaign, these directories provide information about different parameters that is required to precisely process GNSS data.

Compute weekly solution

The data were prepared for each week in order to compute weekly solutions. Bernese GNSS Software V.5.2 was used for processing. Bernese Processing engine processed the provided data. Problems encountered during the process were checked thoroughly manually.

Checking if the weekly solutions were created successfully was done by checking the folder BPE where exactly the problem occurs and in which station. Exploring the /OUT folder to see the summary of the output. RMS of each station was checked and RMS greater than 10mm in E and N and 20mm in U were looked upon. When there is unusual value in RMS, daily solutions were also checked.

A copy of erroneous daily files or even the week was created in OUT folder. These erroneous files were compressed in RAW folder using gzip command and removed from OBS and SOL folders in order to remove error created by them in our solutions. The BPE was run again only with the station with these errors.

Processing and Trend analysis

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The input coordinate time series (Latitude, Longitude and h) obtained from repeated GNSS measurements was converted to E(east), N(north) and U(up) Coordinates, relative to the FIRST observation of a station time series. The trend of the time series was computed based on the specified parameters to be estimated in Iteratively Reweighted Least Squares (IRLS) Algorithm. IRLS is an iterative method in which each step involves solving a weighted least squares problem. IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression as a way of mitigating the influence of outliers.

The trend parameters were polynomial degree (1, 2, 3…), oscillations (oscillations in years (=365.25 days) as vector), Parameter T (logarithmic transient for earthquake events jumps) distinguishes between earthquake event and other jumps like unknown, HW/SW by invoking log.transient.

The seismic deformation can be accommodated by logarithmic or exponential decay formulas independently using estimated parameters (polynomial coefficients, oscillation amplitudes, Heaviside jumps, Logarithmic Transient and Root Mean Square Error RSME for east, north and up component). Earthquake Jump was computed as the sum of Heaviside and Logarithmic Transient

The following is the time series analysis for SIRGAS station MEXI computed in MatLab . It calculates the trend, depending on the given parameters, for selected single station.

Fig: Time Series Analysis for station MEXI

There is an Earthquake in mid-2010 is visible in the time series. This information about earthquake is provided from the jump table.

Conclusion

Development of GNSS and improvement in its accuracy has provided the opportunity to monitor the behavior of tectonic plates or continental movement. Consistently and accurately determined coordinates acquired by Global Navigation Satellite System (GNSS) transformed to a fixed reference epoch over time provides the opportunity to understand time series trend analysis.

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Acknowledgement

This study was done during the internship period at DGFI-TUM. I would like to acknowledge the institute and Dr. Ing Laura Sanchez. Dr. Sanchez (Vice President of GGOS (Global Geodetic Observing System), Chair to Focus Area Unified Height System, Joint Working Group “Implementation of the International Height Reference Frame (IHRF)” and IHRF representative to GGOS Bureau of Products and Standards, Working Group “Towards a consistent set of parameters for the definition of a new GRS” since 2019 for her kind supervision in this work.

References

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• DGFI-TUM. (April 2020). DGFI-TUM. From https://www.dgfi.tum.de/en/about-dgfi-tum/
• Dach R., Lutz S., Walser P., Fridez P. Eds. (2015). Bernese GNSS Software Version 5.2. Astronomical Institute, University of Bern
• Blewitt G, Lavallée D (2002). Effect of annual signals on geodetic velocity. J Geophys Res: Solid Earth, 107(B7), ETG-9.