Global Navigation Satellite System Network Research Articles

Characterization of the co-seismic pattern and slip distribution of the February 06, 2023, Kahramanmaraş (Turkey) earthquakes (Mw 7.7 and Mw 7.6) with a dense GNSS network

Two consecutive earthquakes with the magnitudes of Mw 7.7 and 7.6 (February 06, 2023) occurred on the East Anatolian Fault Zone (EAFZ) segments and unfortunately resulted in significant devastation to human life and cities in Turkey and Syria. In this study, we aimed to analyse the co-seismic displacements and fault slip distributions of these seismic events. Our unique high-spatial-resolution Global Navigation Satellite System (GNSS) network (comprising 73 permanent GNSS stations and 40 campaign observation sites), providing the recent geodetic dataset for the region, allows better constraint of the co-seismic surface displacements and slip distributions of both earthquakes. The three largest total displacements were identified as 466 cm, 362 cm, and 360 cm. The Fault interactions along the EAFZ were obvious during the consecutive earthquakes. The ruptures mainly occurred in the left-lateral components of the fault segments, with the maximum slips of 7.25 m and 9.43 m for the first event along the EAFZ and the second event on the Çardak Fault, respectively.

Revisiting the Variation of the Ionospheric Irregularities in the Low Latitude Region of China Based on Small Regional Geodetic GNSS Station Network

AbstractThe rate of total electron content index (ROTI) and loss of lock (LoL) from global navigation satellite system (GNSS) observations are an important data source for ionospheric scintillation study. However, there are certain limitations of these data in ionospheric scintillation study, and clearing these limitations is important to understand the results from these data. In this paper, the variation of the ionospheric scintillation is revisited based on LoL and ROTI from a spatial dense GNSS network in the low latitude region of China. Via a priori knowledge of scintillation morphology, the method to determine the baseline ROTI for discriminating quiet and disturbed conditions is provided, and the abnormal data of ROTI and LoL unrelated to ionosphere are found and eliminated. Results show that the data examination is necessary. Using the qualified ROTI and LoL, the morphological variations of ionospheric irregularities are revisited. The temporal variation of ROTI and LoL are generally consistent, but there are some discrepancies. The maximum LoL occurrence is at about 21:00 LT, it is between 20:00 and 23:00 LT for ROTI. For spatial distribution, both parameters reach maximum occurrence in the southwest direction, but LoL is more concentrated. The azimuth range of maximum occurrence is 180–190° for LoL and 210–220° for ROTI. Statistically, the correlation between LoL occurrence and ROTI value from observation of single GNSS station is relatively good when ROTI <5 TECU/min, but it becomes worse when ROTI >5 TECU/min. However, their correlation can be greatly improved when data are averaged among all the stations.

Estimation of Height Changes of Continuous GNSS Stations in the Eastern Anatolia Region during the Seasonal Variation

Estimating the height component of Global Navigation Satellite System (GNSS) stations is widely known to be more challenging than estimating the horizontal position. In this study, we utilized height time series data from 37 continuous GNSS stations that were part of the Turkish RTK CORS Network called TUSAGA-Active (Turkish National Permanent GNSS Network Active). The data covered the period from 2014 to 2019, and the selection of stations focused on the Eastern Anatolia region of Turkey due to its topographic characteristics and the pronounced influence of seasonal changes, which facilitated the interpretation of the effects on the height component. The daily coordinates of the GNSS stations were derived using the GAMIT/GLOBK software solution. We identified statistically significant trends, periodic variations, and stochastic components associated with the stations by applying time series analysis to these daily coordinate values. As a result, the vertical velocities of the GNSS stations were determined, along with their corresponding standard deviations. Furthermore, examining the height components of the continuous GNSS stations revealed seasonal effects. We aimed to investigate the potential relationship between these height components and meteorological parameters. The study provides evidence of the interconnectedness between the height components of continuous GNSS stations and various meteorological parameters. Simple linear regression analysis and ARMA time series modeling were utilized to establish this relationship.

GPS + Galileo + BDS-3 medium to long-range single-baseline RTK: an alternative for network-based RTK?

AbstractThanks to the development of the real-time kinematic (RTK) algorithm and the emerging Global Navigation Satellite System (GNSS), especially for Galileo and BeiDou-3, reliable positioning accuracy for medium and long-baseline RTK became possible globally. Moreover, with the development of the GNSS receiver hardware, baseline length limitations due to radio-based communications are removed thanks to internet-based communication. In this work, single-baseline RTK, incorporated partial ambiguity resolution with troposphere and ionosphere weighting, using GPS (G), Galileo (E), BeiDou-3 (C3) and multi-GNSS (GE and GEC3), is conducted with real GNSS data of EUREF Permanent GNSS network under three different cutoff angles (10°, 20°, and 30°) for six different lengths of baselines (~50, ~150, ~250, ~350, ~450, and ~550 km). The results show that the multi-GNSS RTK solution significantly contributed to the positioning accuracy and convergence time of the single-system RTK solutions. Based on the results, non-available epoch-wise solutions for the high-degree cutoff angles are more obvious for the single-system RTK, whereas multi-GNSS solutions provide 100% solutions for each cutoff angle and baseline. The results indicate that instantaneous and a few epochs single-epoch ambiguity resolution is feasible for 50, 150, 250 and 350 km baseline lengths for multi-GNSS RTK. Based on the positioning results, horizontal–vertical positioning improvements of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 50%–37%, 40%–35%, 55%–47%, 53%–54%, 57%–49% and 57%–49% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle. For 20° and 30° cutoff angles, the accuracy improvements are much higher. The convergence time improvements (n/e/u) of multi-GNSS RTK (GEC3) compared with the single-system GPS RTK are found as 86/92/75%, 77/67/72%, 75/77/83%, 53/56/52%, 69/49/62%, and 52/45/39% for 50, 150, 250, 350, 450 and 550 km, respectively, under a 10° cutoff angle.

Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site.

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

Episodic transient deformation revealed by the analysis of multiple GNSS networks in the Noto Peninsula, central Japan

Since November 30, 2020, an intense seismic swarm and transient deformation have been continuously observed in the Noto Peninsula, central Japan, which is a non-volcanic/geothermal area far from major plate boundaries. We modeled transient deformation based on a combined analysis of multiple Global Navigation Satellite System (GNSS) observation networks, including one operated by a private sector company (SoftBank Corp.), relocated earthquake hypocenters, and tectonic settings. Our analysis showed a total displacement pattern over 2 years shows horizontal inflation and uplift of up to ~ 70 mm around the source of the earthquake swarm. In the first 3 months, the opening of the shallow-dipping tensile crack had an estimated volumetric increase of ~ 1.4 × 107 m3 at a depth of ~ 16 km. Over the next 15 months, the observed deformation was well reproduced by shear-tensile sources, which represent an aseismic reverse-type slip and the opening of a southeast-dipping fault zone at a depth of 14–16 km. We suggest that the upwelling fluid spread at a depth of ~ 16 km through an existing shallow-dipping permeable fault zone and then diffused into the fault zone, triggering a long-lasting sub-meter aseismic slip below the seismogenic depth. The aseismic slip further triggered intense earthquake swarms at the updip.

The Four‐Dimensional Variational Neustrelitz Electron Density Assimilation Model: NEDAM

AbstractWith the development of the Global Navigation Satellite System (GNSS) and regional augmentation systems, the influence of the ionosphere on GNSS signals is becoming increasingly important. It is also of particular interest to retrieve the electron density distribution from GNSS observations. We have successfully developed a four‐dimensional variational assimilation scheme, the Neustrelitz Electron Density Assimilation Model (NEDAM), and verified NEDAM by a simulation study using a European ground‐based GNSS network. The performance of NEDAM is validated using two ionosondes and COSMIC‐1 radio occultation observations during the September 2017 geomagnetic storm period. The critical frequency of the F2 layer in NEDAM is much more accurate than that of a physics‐based model driven by observed geophysical indices or the Neustrelitz Electron Density Model (NEDM), when compared to data from two ionosondes. During the storm, the root mean square error of the F2‐layer critical frequency with respect to the two ionosondes is improved by 0.54 and 0.42 MHz, respectively. We also compare two co‐located electron density profiles from the COSMIC‐1 mission with NEDAM and NEDM. It is found that NEDAM is able to reconstruct well not only the peak density but also the peak density height, which is missing in the previous research.

Sequential Generation of Multi-GNSS Multi-Frequency PPP-RTK Products and Their Performance Using the EUREF Permanent GNSS Network

In the classic Precise Point Positioning (PPP) approach, observations of Global Navigation Satellite Systems (GNSS) are processed at the network level to generate satellite clocks and positions. This information can be used to enable accurate point positioning for a single GNSS receiver. In the PPP Real-Time Kinematic (PPP-RTK) approach, satellite phase biases are considered as well, enabling ambiguity resolution at the network and user levels. In this research, 30 s multi-frequency raw GPS, Galileo, and BDS-2/3 observations are processed at the network and user levels in a sequential Kalman filter. PPP-RTK enabling products are generated for up to five frequencies, and ambiguity resolution is performed at the network and user levels using a flexible ambiguity reparameterization approach, comparable to wide- and narrow-laning, which has shown to yield a significantly improved single epoch coordinate solution when multi-frequency observations are available. Different assumptions regarding the time stability of receiver and satellite phase biases have been made and compared. The availability of a precise user coordinate solution when multi-frequency and dual-frequency observations are processed is assessed and compared. A precise ambiguity-fixed solution is available in three epochs or fewer in 77% of all cases with an average of 24 visible satellites for static and kinematic receivers when multi-frequency observations are processed. When only dual-frequency observations are considered, a fixed solution is available in seven epochs or fewer in 71% of all cases. The fastest fixed solution was found in two epochs with multi-frequency observations and in six epochs with dual-frequency observations. Estimating a reference phase clock did not lead to an improvement in the coordinate solution. The findings indicate that a fixed solution can potentially be found faster than often suggested, with potential for further improvement when more satellites or regional atmospheric corrections are considered.

MPG-NET: A low-cost, multi-purpose GNSS co-location station network for environmental monitoring

Global Navigation Satellite Systems (GNSS) are very versatile sensors, which can be used for a variety of commercial and scientific applications. This holds especially true for different fields of remote sensing, such as atmospheric sounding or soil moisture monitoring. With the advent of low-cost dual-frequency GNSS equipment, certain applications are no longer restricted to the use of geodetic-grade instrumentation and can fully take leverage of the measurements in a second frequency band. In view of these emerging benefits, this study introduces the development and deployment of a multi-purpose GNSS station network in the Swiss Alps, called MPG-NET. We discuss the technical details of the station setup, in terms of GNSS hardware and technical design, as well as the quality of derived GNSS remote sensing products. In particular, our analyses focus on the quality of derived time series of zenith total delays (ZTD) and volumetric soil moisture content. Products are validated against benchmark data obtained from numerical weather models and in-situ sensors. For a prototype station, the results show a good agreement with the baseline, with errors of few millimeters for ZTD, and a remarkably high correlation for soil moisture content. Beside the documented value of low-cost GNSS for displacement monitoring (such as landslides or strong earthquakes), these findings are another step towards the establishment of a dense high-precision, multi-purpose GNSS network that comes at a very affordable price.

An improved stacking filtering for extracting the common-mode errors on GNSS coordinate time series in Shanxi

The presence of common mode error (CME) in the coordinate displacement time series of the Global Navigation Satellite System (GNSS) affects geophysical studies using GNSS observations. In order to investigate the effect of CME on the time series in GNSS networks in Shanxi, this paper proposes an improved superposition filtering method by introducing single-day solution accuracy, correlation coefficient, and spherical distance between stations as weights. The filtering effect is evaluated using the GNSS data in Shanxi. By using the improved stacking filtering method, the root mean square (RMS) values for N, E, U are reduced by approximately 27.8%, 29.0%, and 46.0%, respectively. And compared to the traditional stacking filter, our improved method can achieve better results with CME extraction. We investigate the CME spatial-temporal characteristics and its relationship with environmental loading. The results show that the CME between stations decreases as the distance between stations increases. In addition, we analyze the effect of CME on the noise component and velocity estimates. Results show that removing the CME refines the velocity and leads to a significant reduction in the magnitude of noise, indicating that the CME is dominated by the flicker noise in Shanxi Province.

Designing an Optimal GNSS Receiver Network for Space Weather Studies Using Unsupervised Machine Learning Algorithm

AbstractIonospheric maps from an optimal GNSS (global navigation satellite system) receiver network may lead to better understanding and prediction of the equatorial ionospheric gradients. An unsupervised machine learning framework is proposed using the ‐means clustering approach to optimize the available number of GNSS receivers. Beginning with = 15 receivers, the simulations are performed to obtain distribution of the ionospheric pierce points (IPPs) in different grid resolutions up to = 1,000. The algorithm uses a reference grid and subsequently optimizes the locations according to the available IPPs in a cluster. The IPP cluster centers are iteratively optimized based on minimizing the “within cluster sum of squares” error. The IPPs from the optimal receiver locations are used to generate a 2‐D vertical total electron content (VTEC) map using NeQuick‐G model. A one‐sigma confidence limit derived from hourly histogram of the IPPs from the locations is used as decisive criteria for useable grid cells. The number of empty bins thus obtained in a given scenarios provides a percentage normalized empty grid ratio (). A below 10% is used as a threshold to make a decision over eligibility of a scenario to produce a desired VTEC map. Several scenarios are thus tested for varying temporal and spatial grid resolutions, geographical boundaries, GNSS and NavIC satellite configurations, considering the diurnal and annual variation in year 2019. The framework is found to be successful in designing a GNSS network for any geographical region to obtain high resolution 2‐D VTEC maps for space weather applications.

Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection

Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.

Fault displacement analysis using a multidisciplinary approach on the Gerede Segment of the North Anatolian Fault Zone

The North Anatolian Fault Zone (NAFZ) is the world's most seismically significant strike-slip fault and Turkey's most active tectonic structure. A segment of NAFZ known as Gerede is located in northwest Turkey. It is debatable whether the Gerede segment of the NAFZ exhibits evidence of tectonic creep formation. The movement of the Gerede Segment of the NAFZ was investigated using various methods in this study. The Global Navigation Satellite System (GNSS) and soil mechanics-based methods were used in the Gerede Segment of the NAFZ for fault displacement analysis using a multidisciplinary approach. The GNSS technique is widely used in geodynamics-kinematics and cartography studies of earth crust movements, as well as in determining deformations during earthquake periods and as a source of data in defining geological and atmospheric phases. By establishing a GNSS network composed of regional or geodetic station points around the fault zone, it is possible to determine the deformations, velocities, and possible seismic slip rates of permanent GNSS stations on this network. The shear box test is used in soil mechanics to determine the shear strength parameters c and φ. A shear box test is performed to determine the pattern of fault movement (seismic or aseismic) and to determine the shear strength parameters (c, φ). An undisturbed block sample from a paleoseismology trench was used for a consolidated drained (CD) type shear box test. It was found that the fault movement near the Gerede Segment was not creeping. This study makes a new contribution to the debate on whether the Gerede Segment's movement pattern is creep or seismic.

Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland

Abstract. Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %–80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.

Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python package

Dense, regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks enable the monitoring of plate motion and regional surface deformation. The spatial extent and density of these networks, as well as the length of observation records, have steadily increased in the past three decades. Software to efficiently analyze the ever-increasing amount of available timeseries should be geographically portable and computationally efficient, allow for automation, use spatial correlation (exploiting the fact that nearby stations experience common signals), and have openly accessible source code as well as documentation. We introduce the DISSTANS Python package, which aims to be generic (therefore portable), parallelizable (fast), and able to exploit the spatial structure of the observation records in a user-assisted, semi-automated framework that includes uncertainty propagation. DISSTANS is open-source, includes an application interface documentation as well as usage tutorials, and is easily extendable. We present two case studies that demonstrate our code, one using a synthetic dataset and one using real GNSS network timeseries.

Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication

Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios.

Combination Strategy for the Geocentric Realization of Regional Epoch Reference Frames

AbstractFor high‐resolution regional geodetic applications, the International Terrestrial Reference Frame (ITRF) is complemented by regional densifications. These are realized either as multi‐year solutions related to a tectonic plate (e.g., EUREF for Europe) or as epoch reference frames (ERFs) to capture nonlinear geophysical station motions caused by, for example, earthquakes or non‐tidal loading (e.g., SIRGAS for Latin America). These Global Navigation Satellite Systems (GNSS)‐only based regional reference frames have in common that their geodetic datum is aligned with the ITRF datum at a specific epoch. The consequence is that their origin represents the Earth's center of figure and does not coincide with the instantaneous center of mass. Here, we present studies on a direct geocentric realization of regional ERFs. We propose to realize the geodetic datum for each epoch by combining global GNSS, Satellite Laser Ranging, and Very Long Baseline Interferometry networks via measured local ties at co‐located sites. A uniformly distributed global GNSS network is used to realize the orientation via a no‐net‐rotation constraint with respect to the ITRF and is densified by the stations of the regional subnetwork. The developed combination and filtering strategy aims to guarantee a stable datum realization for each epoch‐wise solution. Validating our results against global reference frames and geophysical loading models relating to the Earth's centers of mass and figure, we show that the realized displacement time series are geocentric and reflect seasonal geophysical processes. As the approach does not need to rely on co‐location sites in the region of interest, it is conceptually transferable to other regions on the globe.

Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan

We explored the driving factors of nonlinear signals in vertical coordinate sequences of stations in a Taiwan global navigation satellite system (GNSS) network, including atmospheric loading (ATML), hydrological loading (HYDL), and non-tidal ocean loading (NTOL) effects. At the same time, we used the finite element analysis software MIDAS to quantify the vertical displacements of different types of monuments due to the thermal expansion effect, including deep drilled braced (DDB) and short drilled braced (SDB). By quantitatively comparing the correction results of GNSS time series with different single mass loading models, we found that there was little difference in the correction of different environmental loading products. We compared different combinations of each loading product to correct the GNSS time series, and finally selected the best combination suitable for Taiwan GNSS network, that is, ATML (GFZ_ECMWF IB) + HYDL (IMLS_MERRA2) + NTOL (IMLS_MPIOM06). We found that the spatial and temporal models of ATML and NTOL are very similar, with non-tidal atmospheric loading and non-tidal ocean loading working together, a pattern that may be related to tropical cyclones. Both models also showed good correction effect on GNSS stations in the western plain of Taiwan, but with limited correction effect in the eastern part of Taiwan. This may be due to the influence of the subtropical monsoon climate in Taiwan and the barrier of the central mountain range, resulting in obvious differences between eastern and western Taiwan. The hydrological loading was found to act in the opposite way to the thermal expansion effect in the temporal domain, indicating that some displacements in hydrological loading may cancel out displacements caused by the thermal expansion effect. This aspect of displacement is not included in the hydrological loading model but should be considered when accurately estimating the temporal and spatial variation of water storage capacity in Taiwan using GNSS observed displacements.

Rapid Source Determination of the 2021 Mw 7.4 Maduo Earthquake by a Dense High-Rate GNSS Network in the Tibetan Plateau

Abstract High-rate Global Navigation Satellite Systems (GNSS) observations from an integrated network in the Tibetan plateau were used for rapid source determination of the 2021 Mw 7.4 Maduo earthquake. Constrained by high-rate displacements from nine stations near the source, the magnitude was essentially determined as early as 20 s after the origin time (OT), whereas the 39-s-long rupture propagated only approximately halfway, and together with the epicenter location and rupture length, they were completely determined 29 s after the OT or 10 s before the rupture completion. In addition, to test the capability and feasibility of current GNSS networks for rapid source determination in the Tibetan plateau, two experiments were further implemented to invert source parameters by real observations at farther sites and synthetic observations induced by an Mw 7.0 modeled earthquake. The results demonstrate that current GNSS networks are capable and feasible to provide rapid source determination and earthquake early warning in approximately 30 s after the OT for large earthquakes (Mw 7.0+) based on current station density, spatial coverage and communication conditions in the Tibetan plateau.

A new European service to share GNSS Data and Products

This paper describes the new GNSS data and product services that have been developed within the context of the EPOS (European Plate Observing System) European Research Infrastructure Consortium (ERIC), which is part of the European Strategy Forum on Research Infrastructures. These services, optimized for Solid Earth research applications, endeavour to harmonise, and standardise Global Navigation Satellite System (GNSS) data collection and processing. They have been implemented by the members of the GNSS Data & Products (EPOS-GNSS), one of the Thematic Core Services (TCS) of EPOS with the active support of national and pan-European infrastructures (in particular the Regional Reference Frame Sub-Commission for Europe (EUREF) of the International Association of Geodesy). The optimized use of data from dozens of diverse European GNSS networks, installed not specifically for geodynamic studies, created additional requirements from an organizational and technical point of view, the solutions for which we describe in this article. The data flows from data suppliers and analysis centers to the various TCS Data & Product Portals are described, as well as their integration into the overall EPOS system. This is made through GLASS (GNSS Linkage Advanced Software System), a dedicated software package developed since 2016, whose architecture and functionalities are detailed here. Time series and other GNSS products computed at the several analysis centers are described as are the quality control steps that are performed. Finally, several user cases are presented that demonstrate how different stakeholders (from data providers to scientists) can benefit from the efforts being carried out by the EPOS- GNSS community.