Global Navigation Satellite System Data Research Articles

Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC<sup>4</sup>A

Abstract. In the framework of the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kg m−2, which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kg m−2, reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/, last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.

Long and Short Wavelength of Geodetic Strain Rate Tapering Earthquake Potential in Western Java

Shallow earthquake produced by intra-island fault in a populated region could be a catastrophic disaster. Java island is the most populated area specifically in Western Java since there include Jakarta, the capital city of Indonesia. Cimandiri, Lembang and Baribis fault as main intra-island fault are considered to be responsible due to some shallow historical earthquake in Western Java. However, convergent plate margin such as Java Island generates a broad spectrum of deformation pattern that potentially distracts the deformation due to Baribis fault. In addition, slow relative motion of Sunda block and postseismic deformation due to the 2006 Java tsunami earthquake involve along those fault. Here, we intend to conduct a wavelength decomposition of strain rate calculated from GNSS (Global Navigation Satellite System) data. We expect to separate long wavelength due to wider-region deformation such as deformation due to the interacting plate and postseismic deformation to obtain Short wavelength due to each fault. We utilized the daily position of recent continuous geodetic data to produce strain rate. Therefore, we could apply a moving average filter to extract a short wavelength component. We obtained long wavelength (-150 to 150 nanostrain/yr) in Western Java shows extension indicating the postseismic effect is still continuous while short wavelength (-75 to 75 nanostrain/yr) varies in space.

Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees

ABSTRACT Cut-to-length harvesters collect detailed information on the dimensions and characteristics of individual harvested trees. When equipped with global navigation satellite system (GNSS) receivers and motion sensors, the obtained measurements can be linked to locations of single harvested trees, benefitting a range of forest inventory applications. We propose a way of georeferencing harvested trees using a Komatsu 931XC harvester, which measures and records the machine's bearing, crane angle and crane length for each harvested tree. We replaced the harvester’s standard GNSS receiver with a dual-antenna differential GNSS receiver. From the coordinates obtained, rotations calculated from the GNSS receiver and data on crane length, we determined the location of 285 trees harvested in eight final fellings in Norway. We compared the obtained locations to control measurements taken on the corresponding stumps directly after harvest using a differential GNSS receiver. The mean distance between planimetric coordinates of trees measured by the harvester and corresponding control measurements was 0.88 m with a standard deviation of 0.38 m. By correcting the crane lengths for systematic deviations between harvester and control locations, the mean distance was reduced to 0.79 m. This study shows that measurements of single harvested trees can be georeferenced with sub-meter accuracy, by mounting a differential GNSS receiver on a harvester and without installing additional sensors. The results also suggest that the positional accuracy can be further improved by measuring and recording the length of the telescopic boom, and that with minor adjustments, the system could be fully automated.

Strain characteristics distribution and potential information analysis in Yunnan based on GNSS data

Based on the observation data of 211 Global Navigation Satellite System (GNSS) regional stations from 2011 to 2016, and 59 continuously operating GNSS stations in the Yunnan region of China since 2013, we analyzed the strain characteristics of Yunnan from the following two perspectives. On the one hand, we analyzed the strain characteristics and tried to determine the potentially abnormal areas of Yunnan based on a multi-scale spherical wavelet method, from an overall strain background view. On the other hand, we investigated the relationship between the strain time series and the potentially abnormal areas of Tonghai County through an earthquake case study, and we attempted to extract the potentially information before the earthquake from the strain time series based on Hilbert-Huang transform (HHT), from a local strain time-series view. The results showed that: (1) in the integrated solution processing of the regional strain patterns, the spherical wavelet method can adapt to the spatial variability in station density and estimate the strain patterns at multiple scales; and (2) by combining the long-term characteristics of the strain time series, the grid division, the two times standard deviation constraint, and the HHT dynamic analysis, it gives the some potential information details on the eve of an earthquake such as the Tonghai earthquake.

Commentary: The Role of Geodetic Algorithms for Earthquake Early Warning in Cascadia

AbstractThe ShakeAlert earthquake early warning (EEW) system issues public alerts in California and will soon extend to Oregon and Washington. The Cascadia subduction zone presents significant new challenges and opportunities for EEW. Initial publications suggested that EEW algorithms based on Global Navigation Satellite System (GNSS) data could provide improved warning for intraslab events and dramatically improved warning for offshore megathrust events, both of which contribute significantly to hazard in Cascadia. We find that some expectations in these publications were unrealistic, and we demonstrate that in general geodetic algorithms would not produce timely warnings for intraslab events nor warning times of two minutes or more for severe shaking from megathrust earthquakes. Nonetheless, lessons from recent earthquakes in Japan and California, for which alerts from seismic algorithms suffered from magnitude saturation and high data latencies, demonstrate the urgent need for rigorous testing of geodetic EEW as a potential complement to seismic EEW.

The MADMAX data set for visual‐inertial rover navigation on Mars

AbstractPlanetary rovers increasingly rely on vision‐based components for autonomous navigation and mapping. Developing and testing these components requires representative optical conditions, which can be achieved by either field testing at planetary analog sites on Earth or using prerecorded data sets from such locations. However, the availability of representative data is scarce and field testing in planetary analog sites requires a substantial financial investment and logistical overhead, and it entails the risk of damaging complex robotic systems. To address these issues, we use our compact human‐portable DLR Sensor Unit for Planetary Exploration Rovers (SUPER) in the Moroccan desert to show resource‐efficient field testing and make the resulting Morocco‐Acquired data set of Mars‐Analog eXploration (MADMAX) publicly accessible. The data set consists of 36 different navigation experiments, captured at eight Mars analog sites of widely varying environmental conditions. Its longest trajectory covers 1.5 km and the combined trajectory length is 9.2 km. The data set contains time‐stamped recordings from monochrome stereo cameras, a color camera, omnidirectional cameras in stereo configuration, and from an inertial measurement unit. Additionally, we provide the ground truth in position and orientation together with the associated uncertainties, obtained by a real‐time kinematic‐based algorithm that fuses the global navigation satellite system data of two body antennas. Finally, we run two state‐of‐the‐art navigation algorithms, ORB‐SLAM2 and VINS‐mono, on our data to evaluate their accuracy and to provide a baseline, which can be used as a performance reference of accuracy and robustness for other navigation algorithms. The data set can be accessed at https://rmc.dlr.de/morocco2018.

NOANET: A Continuously Operating GNSS Network for Solid-Earth Sciences in Greece

Abstract The NOANET network is a continuously operating high-rate Global Navigation Satellite System (GNSS) network in Greece whose primary role is to enhance and support geophysical research employing GNSS data. This network is operated by the Institute of Geodynamics of the National Observatory of Athens and currently (September 2020) consists of 26 stations, most of which are located close to major seismogenic structures of Greece to optimally measure tectonic and seismically induced motions. All NOANET receivers are configured to record and collect data with a sampling rate of 1 Hz, although some of them also collect data every 5 Hz on their ring buffer. The network is committed to free and open data sharing within the scientific community, and the collected data are made available via the NOANET data repository and distribution point for all interested parties with no limitations. Integrity, validity, and quality checks of the acquired data are performed using a variety of software tools along with in-house developed programs to supervise the network performance and detect ill-formed data and/or awkward station behavior. In addition, the conventional low-rate GNSS observation data of all NOANET stations are routinely processed on a daily basis to supervise their performance through their position time series. Since the beginning of its establishment, the NOANET network has recorded a variety of deformation signals, and a large number of published papers have used GNSS data from stations that are part of NOANET to constrain, model, and interpret the nature of the associated geophysical phenomena.

Repeated UAV Campaigns, GNSS Measurements, GIS, and Petrographic Analyses for Landslide Mapping and Monitoring

Landslides are posing a significant global hazard as they occur instantaneously with devastating consequences. The development of new remote sensing technologies and innovative processing techniques over the past few years opened up new horizons and perspectives in landslide monitoring research. The purpose of the current research is the integrated monitoring of an active landslide, located in Western Greece, using low-cost and high-repeatability remote sensing data like those obtained by unmanned aerial vehicles (UAVs). Repeated UAV campaigns and global navigation satellite systems (GNSS) surveys were performed to assess the activity of the landslide and determine its kinematic behavior. UAV data were processed using structure from motion (SfM) photogrammetry and the generated high-detailed orthophotos and digital surface models (DSMs) were submitted in further processing procedure in an ArcGIS environment. Regarding the GNSS data, a new low-cost technique for the estimation of the direction and the rate of movement of the displaced material was developed. The repeated measurements were displayed in a vector format in a three-axis diagram. In addition, GNSS measurements were used to verify the results of the photogrammetric processing. The final assessment was carried out taking into account geological data such as petrographic and crystallographic features of the material of the landslide. It was observed that the lithology and consequently the petrographic properties of the material plays a key role regarding the activity of the landslide.

Determination of Orthometric Height using GNSS and EGM Data: a Scenario of the Federal University of Technology Akure

The advent of Global Navigation Satellite System (GNSS) and Earth Gravitational Model (EGM)particularly Global Positioning System (GPS) has modernized geodetic surveying in providing horizontal and vertical positions of points with a sub-meter level of accuracy over the reference ellipsoid. The GPS gives ellipsoidal heights which makes the conversion of the heights to orthometric heights possible by incorporating a geoid model. The conventional method of determining orthometric height is tedious, time-consuming, and labour intensive. This study entails the determination of orthometric height using GNSS and EGM data. A total of forty-nine (49) stations selected within the study area were occupied for GPS observation using South DGPS instrument in static mode for the position and ellipsoidal height determination. The geoidal height values of the GPS derived data were computed using GeoidEval utility software with reference to three different EGMs (EGM2008, EGM96 and EGM84). In order to determine the orthometric heights of the selected stations, the difference between the EGM geoidal height values (NEGM) and the ellipsoidal heights were computed. The results show that the orthometric height obtained with respect to EGM2008 gives better results with a standard deviation of 9.530m and a standard error of 1.361m. The study reveals that the use of GNSS and EGM data for orthometric height determination is less expensive, less tedious, accurate and time-saving compared to the conventional approach of geodetic and spirit levelling.

Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods.

Data Assimilation for Tsunami Forecast With Ship‐Borne GNSS Data in the Cascadia Subduction Zone

AbstractAn efficient and cost‐effective near‐field tsunami warning system is crucial for coastal communities. The existing tsunami forecasting system is based on offshore Deep‐Ocean Assessment and Reporting of Tsunamis and Global Navigation Satellite System (GNSS) buoys which are not affordable for many countries. A potential cost‐effective solution is to utilize position data from ships traveling in coastal and offshore regions. In this study, we examine the feasibility of using ship‐borne GNSS data in tsunami forecasting. We carry out synthetic experiments by applying a data assimilation (DA) method with ship position (elevation and velocity) data. Our findings show that the DA method can recover the reference model with high accuracy if a dense network of ship elevation data is used. However, the use of ship velocity data alone is unable to recover the reference model. In addition, we carried out sensitivity studies of the DA method to the ship spatial distribution. We find that a 20 km gap between the ships works well in terms of accuracy and computational time for the example source model that we explored. The highest accuracy is obtained when data from a sufficient number of ships traveling in and around the tsunami source area are available.

Analysis of Latitude Observations and Data of Satellite Navigation Systems to Determine Geodynamic Parameters

This study is focused on the analysis of data of the long-term latitude observations and the Global Navigation Satellite System (GNSS). The latitude observations include small-amplitude periodic and random harmonics as noise components of different nature and duration. These harmonics are a source of valuable information on seismology, geodynamic processes, and Earth structure in the telescope vicinity. The value of observations carried out at a certain observatory is in their reliability, homogeneity, uniqueness, and long-term series. The latitude observations are reduced to build a diagram of temporal variations in the mean latitude of an observational site on a long-time interval. In this paper, we analyze a curve of the nonpolar oscillations (free of components induced by the pole motion) of the mean latitude. We compare the periodic components of secular variations in the mean latitude and the GNSS observations. Geodynamic parameters of the dynamics of the Earth’s crust relative to tectonic faults on the Tatarstan territory have been determined from the GNSS data; also, from the analysis of the astronomical latitude variations anomalously deviating from the predictive model, the correlation with seismic processes has been shown.

Feasibility of retrieving effective reflector height using GNSS-IR from a single-frequency android smartphone SNR data

Global Navigation Satellite Systems (GNSS) have been routinely used for geodetic-based survey and mapping studies such as precise point positioning, landslide, earthquake and crustal deformation monitoring, engineering surveys, in short, where accurate positioning is required. To do that, the GNSS observables should be eliminated from the error sources. Among the error sources affected to GNSS data, multipath stands for one of the largest error sources and should be removed. This multipath affects the strength of received microwave signal or signal-to-noise ratio (SNR), which is recorded at the GNSS antenna. As of now, many studies are successfully conducted to retrieve reflector height from signal-to-noise ratio (SNR) data gathered with a geodetic GNSS receiver. However, GNSS SNR data collected from a smartphone may also show a pattern for interferences of the direct and reflected signals from where the aforementioned heights are retrieved. In this study, an experimental site was setup to retrieve effective reflector height from a permanent mast attaching both smartphone with single frequency and geodetic GNSS receiver. The GNSS SNR data were gathered by the same time interval for a specific vertical height from the ground for three days and for approximately 5-h daily observation durations. The validation of the estimated reflector heights was performed by the root mean square error (RMSE) analysis. The RMSEs of estimated reflector heights for Xiaomi Mi 8 Lite and Trimble NetR9 geodetic receiver are computed as 1.9 and 3.7 cm, respectively. The initial results show that the outcomes of the analysis agree well with whether the in-situ measurements or the mean values via RMSEs. According to the performance assessments for accurate height detection and cost efficiency, single-frequency smartphones can be used to extract the reflector height from the collected raw SNR data.

A Regional Zenith Tropospheric Delay (ZTD) Model Based on GPT3 and ANN

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions.

Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking.

The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring. In order to develop and validate this methodology, we have set up an experimental case study, relying on the use of an industrial six-axis robot arm, on which the instruments are mounted. The robot simulates the structural motion resulting atop a wind-excited wind turbine tower. The quality of the 6C KF reconstruction is assessed by comparing the estimated response to the feedback system of the robot, which performed the experiments. The fusion of rotational information yields significant improvement for both the acceleration recordings but also the GNSS positions, as evidenced via the substantial reduction of the RMSE, expressed as the difference between the KF predictions and robot feedback. This work puts forth, for the first time, a KF-based fusion for all six motion components, validated against a high-precision ground truth measurement. The proposed filter formulation is able to exploit the strengths of each instrument and recover more precise motion estimates that can be exploited for multiple purposes.

On Trading the Spreading Gain With the Coding Rate and Its Application to GNSS Data Component Design

The ubiquity of global navigation satellite system (GNSS)-based positioning and timing services is often frustrated by the necessity to operate in harsh environments, where the carrier-to-noise ratio is low, and hence, decoding of navigation data and even tracking of an acquired symbol are difficult. We consider the possibility of improving the decoding performance of the GNSS data component by trading the spreading gain against the coding rate. The rationale is that spreading codes can be seen as a form of repetition coding that can be (at least partially) replaced by more robust coding forms to improve robustness to (any form of) noise. This is true both for the classical additive white Gaussian noise channel case and for more realistic GNSS channel formats severely degraded by multiple access interference and near-far effects. By bringing results on finite-block-length channel capacity and coding rates from information/communication theory to the GNSS domain, we are able to establish and discuss the expected performance gap, as well as the limits of such tradeoff.

Global Ionospheric Modeling Using Multi-GNSS and Upcoming LEO Constellations: Two Methods and Comparison

Global ionosphere model with high accuracy and resolution is of great importance for ionospheric research and global navigation satellite system (GNSS) precise positioning applications. In the last 20 years, the accuracy and reliability of global ionospheric model benefitted from the development of GNSS technology have been improved significantly, but its performance is still not good over many regions (e.g., oceans and polar region) due to the lack of ground GNSS stations. Fortunately, the rapid development of low earth orbit (LEO) satellite constellations provides a potential opportunity to address this issue. However, there is still no optimal approach to make use of the LEO-based ionospheric observations because of the different observation ranges compared with that of ground-based GNSS ionospheric observations. In this article, two approaches are proposed to combine GNSS and LEO observation data for ionosphere modeling, which are single-layer normalization (SLN) method and dual-layer superposition (DLS) method, respectively. The results exhibit a significant improvement of ionospheric model accuracy by combining LEO and GNSS observation data based on our proposed methods compared with that using GNSS data only, with a reduction in root mean square (rms) error of about 25% and 21% for SLN method and DLS method, respectively. The article also highlights the relations between the performance of ionospheric model estimated by the SLN method and LEO ionospheric observations with different observation accuracy and different satellite cut-off elevations. The results indicate that ionospheric model estimated by GNSS/LEO using SLN method improves at least 25% compared with that by GNSS only. The improvement of ionospheric model estimated with the cut-off elevation of 50° is the best, followed by 70°, and then 20°.

Cooperative Positioning for V2X Applications Using GNSS Carrier Phase and UWB Ranging

With the evolution of communication techniques, vehicle-to-vehicle (V2V) communications with low latency and high speed facilitate the study of cooperative positioning (CP) methods for vehicular applications. However, the current CP methods are incapable of bridging the gap between positioning accuracy required for crucial vehicular applications and what CP provides. The CP method with carrier-phase-based Real Time Kinematic (RTK) principle becomes a potential candidate for high-precision positioning. In this letter, we reveal a cooperative navigation filtering (CNF) algorithm fusing Global Navigation Satellite System (GNSS) data and Ultra-wideband (UWB) ranging data to achieve decimeter-level positioning. The main novelty of the proposed CP method is to include centimeter-level GNSS carrier phase information and derive a collaborative ambiguity resolution model. Numerical results and comparisons validate the feasibility and superiority of proposed method.

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

Global Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events. The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC. Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms. We apply the TVA to the Mw 8.3 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario. Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 9.5 min after the rupture respectively. The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source. The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation.