GEODETIC SOFT- AND HARDWARE SYSTEMS BASED ON PERSPECTIVE DOMESTIC GNSS RECEIVERS

In the article the current problems and the perspectives of compound monitoring and positioning systems based on domestic Russian GNSS receivers are considered. Modernized concepts of those systems are proposed for the issues solving by the perspective GNSS receivers and geodetic net software. The systems based on those technologies are also overviewed. The conclusions on specifications, form-factors and the features of potential receivers are made. Four models of potential GNSS receivers are given based on earlier research and experimental design developments of Russian Institute of Radio-navigation and Time (RIRT). The perspective software modifications for various-purpose GNSS networks are also overviewed. The propositions are summarized with conceptual systems of local automatized geodetic network and the system of plan-height position control. The structure and features of the systems are overviewed. The conclusions of the works were made and the future continuation of the works are given.

GNSS Networks for Geodynamics in the Caribbean, Northwestern South America, and Central America

For several years, under the framework of national and international projects, the number of GNSS geodetic stations has been increasing in countries located in the area comprised by the Caribbean, northwestern South America and Central America. Data from these geodetic stations have made it possible not only to meet the needs for geospatial information in each of the countries, but also to get a better understanding about the geodynamic interaction of the Caribbean, South American, Nazca and Cocos plates, as well as tectonic blocks wedged in between these plates. This article presents a brief description of the tectonic framework, the existing geodetic networks and the results obtained using data from some stations in the study area.

M3G: an expanding catalogue of permanently tracking GNSS stations in Europe

<p>The Metadata Management and Distribution System for Multiple GNSS Networks (M<sup>3</sup>G, https://gnss-metadata.eu), hosted by the Royal Observatory of Belgium, is one of the services of the European Plate Observing System (EPOS, https://www.epos-eu.org) and EUREF (http://euref.eu).</p><p>M<sup>3</sup>G provides the scientific as well as the non-scientific community with a state-of-the-art archive of information on permanently tracking GNSS stations in Europe, including the station description, the GNSS networks the stations contribute to, whether station observation data are publicly available, and how to access them. </p><p>Since its first public release (2018), M<sup>3</sup>G has been under continuous development, to respond to the evolving needs of the GNSS community, to progress towards FAIR data principles and comply with GDPR. </p><p>M<sup>3</sup>G offers APIs and an interactive user interface where any GNSS station manager, after registration, can insert all information relative to its GNSS stations and make this information publicly available. Consequently, the commitment of station managers to insert GNSS station metadata in M<sup>3</sup>G and their willingness to keep the information up to date is crucial for the success of M<sup>3</sup>G.</p><p>At the moment, M<sup>3</sup>G is used by 127 GNSS agencies and includes data from more than 2500 GNSS stations all over Europe, and more still in the process of being collected.</p><p>We will illustrate the rationale underlying M<sup>3</sup>G, the data that it provides and how these data can be accessed.</p>

A new spatio-temporal graph neural network method for the analysis of GNSS geodetic data

<p>Graph neural networks are a newly established category of machine learning algorithms dealing with relational data. They can be used for the analysis of both spatial and/or temporal data. They are capable of modeling how time series of nodes, which are located at different spatial positions, change by the exchange of information between nodes and their neighbors. As a result, time series can be predicted to future epochs.</p><p>GNSS networks consist of stations at different locations, each producing time series of geodetic parameters, such as changes in their positions. In order to successfully apply graph neural networks to predict time series from GNSS networks, the physical properties of GNSS time series should be taken into account. Thus, we suggest a new graph neural network algorithm that has both a physical and a mathematical basis. The physical part is based on the fundamental concept of information exchange between nodes and their neighbors. Here, the temporal correlation between the changes of time series of the nodes and their neighbors is considered, which is computed by geophysical loading and/or climatic data. The mathematical part comes from the time series prediction by mathematical models, after the removal of trends and periodic effects using the singular spectrum analysis algorithm. In addition, it plays a role in the computation of the impact of neighboring nodes, based on the spatial correlation computed according to the pair-wise node-neighbor distance. The final prediction is the simple weighted summation of the predicted values of the time series of the node and those of its neighbors, in which weights are the multiplication of the spatial and temporal correlations.</p><p>In order to show the efficiency of the proposed algorithm, we considered a global network of more than 18000 GNSS stations and defined the neighbors of each node as stations that are located within the range of 10 km. We performed several different analyses, including the comparison between different machine learning algorithms and statistical methods for the time series prediction part, the impact of the type of data used for the computation of temporal correlation (climatic and/or geophysical loading), and comparison with other state-of-the-art graph neural network algorithms. We demonstrate the superiority of our method to the current graph neural network algorithms when applied to time series of geodetic networks. In addition, we show that the best machine learning algorithm to use within our graph neural network architecture is the multilayer perceptron, which shows an average of 0.34 mm in prediction accuracy. Furthermore, we find that the statistical methods have lower accuracies than machine learning ones, as much as 44 percent.</p>

A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network

The Global Navigation Satellite System (GNSS) tomographic technique can be used for remote sensing of the three-dimensional water vapor (WV) distribution in the troposphere, which has attracted considerable interest. However, a significant problem in this technique is the excessive reliance on constraints (particularly in large GNSS networks). In this paper, we propose an improved tomographic method based on optimized voxel, which only considers the voxels passed by GNSS rays. The proposed method can completely prevent the tomographic algorithm interference of constraints that originated from empirical functions. Experiments in Nanjing in the periods of day-of-year (DOY) 182–184, 2019, and 244–246, 2019, show that the mean absolute error (MAE) and root mean square error (RMSE) of the WV density profile obtained using the proposed method are 0.9 and 1.3 g/m3, while those obtained using the conventional method are 1.3 and 1.8 g/m3, respectively, with respect to the radiosonde (RS) method. The numerical results show that the proposed method is reliable and has a superior accuracy to that of the conventional method.

ITRF use for global applications and estimation of linear velocities of dense networks

<p>GNSS networks are required to continue meeting the ever-increasing demand for global positioning applications operating in a global reference frame. Meanwhile, the requirements of applications based in a local (regional) official reference frame must still be met. Using Bernese GNSS software (Dach, 2015), we can process GNSS networks in the ITRF2014 reference frame and, using Leica GNSS Spider, deliver GNSS corrections in ITRF2014, whilst continuing to serve those with local demands.  To maintain high precision of the GNSS network we perform a daily solution, which is computed based on precise orbits and following the guidelines of the EPN Analysis Centres. To ensure the daily solution runs with correct data, we maintain a database of all reference station equipment changes. Using the daily solution, we are estimating the linear velocity of reference stations within GNSS networks, and are also considering jumps due to equipment changes. The estimated velocities give the opportunity to monitor the long-term stability of the network as well as the quality of reference station coordinates. The daily solution and monitoring of GNSS networks are executed by the Leica Geosystems solution named Leica CrossCheck, which is based on Bernese GNSS software. Leica CrossCheck is capable to monitor GNSS networks of all scales. This includes the computation and monitoring of approximately 5000 GNSS reference stations worldwide, including those part of the HxGN SmartNet GNSS network.  </p><p>KEYWORDS: GNSS reference station network, Bernese GNSS 5.2, Leica CrossCheck, Leica GNSS Spider, HxGN SmartNet <br> </p><p>References: <br>Dach, R., S. Lutz, P. Walser, P. Fridez (Eds); 2015: Bernese GNSS Software Version 5.2. User manual, Astronomical Institute, Universtiy of Bern, Bern Open Publishing. DOI: 10.7892/boris.72297; ISBN: 978-3-906813-05-9.   <br> </p>

GNSS Station Metadata Revisited in Response to Evolving Needs

<p>The IGS (International GNSS Service) site log format is the worldwide standard for exchanging GNSS station metadata. It contains, among other things, a description of the GNSS site and its surroundings, the contact persons, and an historical overview of the GNSS equipment. This information is valuable for reliable GNSS data analysis and interpretation of the results.</p><p>This IGS site log is also used within the EUREF Permanent Network (EPN, Bruyninx et al., 2019) and the GNSS component of the European Plate Observing System (EPOS, https://www.epos-eu.org/). However, due to their specific needs, these networks collect additional GNSS metadata. For example, within the EPN, individual receiver antenna calibration values are collected, as well as the information on the data provided by the station. EPOS is collecting in addition data licences. Within the Creative Commons permitted licence scheme, two licences will be adopted by EPOS, CC:BY and CC:BY:NC. Both licenses require that the data user acknowledges (cites) the data owner. To facilitate this data citation, EPOS recommends attributing Digital Object Identifiers (DOI) to the GNSS data and therefore also includes the DOI in the collected GNSS station metadata.</p><p>Many IGS and EPN stations also contribute to EPOS and therefore it is imperative to harmonize the collection and distribution of the additional metadata. The GeodesyML (http://geodesyml.org) format already allows including more metadata compared to the IGS site log format. In this poster, we will review the challenges and propose how to tackle them. We will finish by showing the choices made within the “Metadata Management and Distribution System for Multiple GNSS networks” (M<sup>3</sup>G) which collects and disseminates GNSS station metadata within both the EPOS and EPN networks.</p><p>Bruyninx C., Legrand J., Fabian A., Pottiaux E. (2019) GNSS Metadata and Data Validation in the EUREF Permanent Network. GPS Sol., 23(4), https://doi: 10.1007/s10291-019-0880-9          </p>

Evaluation of Earthquake Magnitude Estimation and Event Detection Thresholds for Real-Time GNSS Networks: Examples from Recent Events Captured by the Network of the Americas

Several studies have shown that real-time (RT) Global Navigation Satellite Systems (GNSS) measurements can provide an estimate of an earthquake’s moment magnitude (Mw) using scaling laws that relate peak ground displacements (PGDs) and hypocentral distance with Mw. In this study, we use data from GNSS stations operated by UNAVCO as part of the National Science Foundation Geodetic Facility for the Advancement of Geoscience (GAGE) that comprises the Network of the Americas (NOTA) to show that precise point positioning (PPP) solutions distributed in RT during five recent earthquakes could be used to calculate Mw rapidly and reliably. We analyze solutions distributed by UNAVCO during the 8 September 2017 Mw 8.2 Tehuantepec, Mexico, 10 January 2018 Mw 7.5 Great Swan Island, Honduras, 23 January 2018 Mw 7.9 Gulf of Alaska earthquake, and the 4 July 2019 Mw 6.4 and 6 July 2019 Mw 7.1 Ridgecrest, California, earthquakes. We find that RT-GNSS Mw estimates consistent with Advanced National Seismic System Comprehensive Earthquake Catalog values are available tens of seconds to a few minutes after the event origin time. The speed with which an estimate is available is dependent on the proximity to the epicenter of the closest NOTA stations. The results demonstrate that RT-GNSS networks could be used to mitigate the problem of magnitude saturation observed in seismic-based earthquake early warning (EEW) systems. RT-GNSS effectively expands the spectrum of events for which a seismic EEW system can provide accurate warnings of impending ground shaking and provides an independent verification of seismically derived magnitudes. We also analyze the RT-PPP solutions from more than 800 RT-GNSS stations to determine the ambient-noise levels of each NOTA station and combine that with PGD magnitude scaling laws to construct regional network sensitivity maps for the NOTA network. Such maps may be used to determine “blind spots” or regions of lower sensitivity in RT-GNSS networks under consideration for EEW.