Tectonic Geodesy as a Supplement Data in Seismology

Observations from permanent GNSS stations are actively used for the research and monitoring of geodynamic processes. Today, with the use of modern scientific programs and IGS products, it is possible to determine GNSS station coordinates and velocities at the level of a few millimeters. However, the scientific community constantly faces the question of increasing the accuracy of coordinate definitions to obtain more reliable data in the study of geodynamic phenomena. One of the main sources of errors is systematic measurement errors. To date, the procedure for their removal is still incomplete and imperfect. Also, during the processing of long-term GNSS measurements, it was found that the coordinate time series, after the removal of trend effects, are also characterized by seasonal variations, mainly of annual and semi-annual periods. We estimated the daily coordinate time series of 10 permanent GNSS stations in the central-eastern part of Europe from 2001 to 2019 and calculated the seasonal variation coefficients for these stations. The average value of the coefficients for the annual cycle for the N, E, and H components is −0.7, −0.2, and −0.7 mm, and for the semi-annual cycle the average value is 0.3, 0.4, and −0.5 mm. The obtained coefficients are less than 1 mm, which is why it can be argued that there is no seasonal component in the coordinate time series or that it is so small that it is a problematic task to calculate it. This practical absence of a seasonal component in long-term time series of GNSS coordinates, in our opinion, is partly compensated by the use of modern models of mapping functions (such as VMF3) for zenith tropospheric delays instead of the empirical GMF. To test the obtained results, we calculated the coefficients of seasonal variations for the sub-network of GNSS stations included in the category of the best EPN stations—C0 and C1. The values of the coefficients for the stations of this network are also less than 1 mm, which confirms the previous statement about the absence of a seasonal component in the long-term time series of coordinates. We also checked the presence of seasonal changes in the time series using the well-known decomposition procedure, which showed that the seasonal component is not observed because the content does not exceed 10% for additive decomposition and 20% for multiplicative decomposition.

Artificial corner reflectors (CRs), passive (which have no electronic parts), or active ones, so called electronic CR (ECR) or compact transponders (CAT), are devices which reflect the radar signal back to the SAR satellites and provide measurement points at desired locations. Using, such devices we can measure temporal Line of sight (LOS) changes of the CRs using the InSAR technique and for example monitor the ground movements precisely.Since January 2020, Lantmäteriet, the Swedish mapping, cadastral and land registration authority, has installed three ECRs and several types of passive reflectors (different shape and size, planned for C-band Sentinel-1 satellites) in different locations in Sweden. So far, ECRs are still functioning with no electronic failure. However, from the ESA Geodetic SAR project (https://eo4society.esa.int/projects/sar-hsu/) we experienced that the ECRs electronic characteristics are different, so individual calibrations maybe required by the manufacturer. In addition, thermal effects may also cause problems for measurements with ECRs. Therefore, instead of installing more ECRs, we switched to passive ones which have no electronics and have already shown their high-quality performance in different studies. So far, we have installed ten CRs in different locations and the goal is to continue and complement the national geodetic infrastructure of Sweden with at least twenty passive reflectors which are co-located with permanent GNSS stations. Among others, these co-located corner reflectors can potentially contribute to the development of the national and European ground motion services in future updates. Moreover, the co-location helps to map the relative ground motions estimated with InSAR to an absolute geodetic reference frameAmong different tests and performance analysis of such reflectors, we did multipath analysis to investigate if our corner reflectors cause any multipath error on nearby GNSS stations.  We looked at the coordinate time series of the twin GNSS stations at two locations, Visby and Sveg. The installed corner reflector, double back-flipped squared, in Sveg is about 6 m away from the GNSS stations whereas, in Visby, the twin corner reflectors, ascending and descending, are about 20 meters away and have a trihedral squared trimmed shape. The daily GNSS coordinate time series for three components before and after installation of the corner reflectors didn’t show any significant jump in the time series and the coordinate variations are in the range of expected mm-level variations for all stations.

The paper analyzes the vertical displacements of the GNSS sites of civil engineering structures caused by non-tidal atmospheric loading (NTAL). The object of the study is the Dnister Hydroelectric Power Plant №1 (HPP-1) and its GNSS monitoring network. The initial data are the RINEX-files of 14 GNSS stations of the Dnister HPP-1 and 8 permanent GNSS stations within a radius of 100 km, the NTAL model downloaded from the repository of German Research Centre for Geosciences GFZ for 2019-2021, and materials on the geological structure of the object. Methods include comparison and analysis of the altitude component of GNSS time series with model values of NTAL as well as interpretation of the geodynamic vertical displacements, taking into account the analysis of the geological structure. As a result, it was found that the sites of the GNSS network of the Dnister HPP-1 undergo less vertical displacements than the permanent GNSS stations within a radius of 100 km. This corresponds to the difference in thickness and density of the rocks under the GNSS sites and stations, so they undergo different elastic deformations by the same NTAL. In addition, the research detected different dynamics of vertical displacements of GNSS sites on the dam and on the river banks. It leads to cracks and deformations of concrete structures in the dam-bank contact zones. During the anomalous impact of NTAL, the altitude of even nearby sites can change if the geological structure beneath them is different. The work shows that for civil engineering structures it is necessary to apply special models to take into account NTAL deformations for high-precision engineering and geodetic measurements.

<p>The Southern Patagonia Icefield (SPI) is the largest continuous ice mass in Southern Hemisphere outside Antarctica. It has been shrinking since the little Ice Age (LIA) period with increasing rates in recent years. In response to this deglaciation process an uplift crustal deformation has been expected. In order to test this hypothesis, a number of GNSS stations installed at both side of the international border between Chile and Argentina, have been repeatedly measured in recent decades yielding vertical velocities up to 41 mm/a.     The obtained horizontal velocities have also shown that GIA is only one of the main components been the tectonic deformations the other factors, including the western interseismic tectonic deformation field related to plate subduction (Richter et al., 2016).</p><p>We addressed this hypothesis by installing two permanent GNSS stations in nunataks located in the northern half of the SPI. The first one called ECRG was setup up within the accumulation area at 1417 m asl and was measuring with several interruptions due to power supply between 2015/10/24 and 2018/06/18, yielding a total of 371 days with data. The second station called ECGB was installed at 1610 m asl in 2015/10/08 and was continuously measuring also with interruptions until 2019/05/28, with a total of 542 measured days. The stations were equipped with a Trimble NetR9 receiver and a Trimble Zephyr (TRM41249.00) antennae without protective radomes. The collected data was processed with the Bernese v5.0 software and the data were linked to the International GNSS Service 2008 (IGS08) permanent stations.</p><p>The preliminary results indicate vertical velocities of 33.03 ±2.14 mm/a at ECRG and 36.55±2.58 mm/a at ECGB. The mean horizontal velocities reached 11.7 mm/a with an azimuth of 43º. These results are within the maximum values obtained in previous studies that measured nearby stations for short periods of time in several occasions. The high vertical velocities and their spatial distribution are a clear indication of the GIA response of this part of Southern Patagonia.</p><p>Reference</p><p>Richter, A., Ivins, E., Lange, H., Mendoza, L., Schröder, L., Hormaechea, J. L., … Dietrich, R. (2016). Crustal deformation across the Southern Patagonian Icefield observed by GNSS. Earth and Planetary Science Letters, 452, 206–215. https://doi.org/10.1016/j.epsl.2016.07.042</p>

The Wanshan calibration site (WSCS) is the first in-situ field for calibration and validation (Cal/Val) of HY-2 satellite series in China. It was built in December, 2018 and began business operation in 2020. In order to define an accurate datum for Cal/Val of altimeters, the permanent GNSS station (PGS) data of the WSCS observed on Zhiwan (ZWAN) and Wailingding (WLDD) islands were processed using GAMIT/GLOBK software in a regional solution, combined with 61 GNSS stations distributed nearby, collected from the GNSS Research Center, Wuhan University (GRC). The Hector software was used to analyze the trend of North (N), East (E), and Up (U) directions using six different noise models with criteria of maximum likelihood estimation (MLE), Akaike Information Criteria (AIC), and the Bayesian Information Criteria (BIC). We found that the favorite noise models were white noise plus generalized Gauss–Markov noise (WN + GGM), followed by generalized Gauss–Markov noise (GGM). Then, we compared the PGS velocities of each direction with the Scripps Orbit and Permanent Array Center (SOPAC) output parameters and found that there was good agreement between them. The PGSs in the WSCS had velocities in the N, E, and U directions of −10.20 ± 0.39 mm/year, 31.09 ± 0.36 mm/year, and −2.24 ± 0.66 mm/year for WLDD, and −10.85 ± 0.38 mm/year, 30.67 ± 0.30 mm/year, and −3.81 ± 0.66 mm/year for ZWAN, respectively. The accurate datum was defined for Cal/Val of altimeters for WSCS as a professional in-situ site. Moreover, the zenith wet delay (ZWD) of the coastal PGSs in the regional and sub-regional solutions was calculated and used to validate the microwave radiometers (MWRs) of Jason-3, Haiyang-2B (HY-2B), and Haiyang-2C (HY-2C). A sub-regional PGS solution was processed using 19 continuous operational reference stations (CORS) of Hong Kong Geodetic Survey Services to derive the ZWD and validate the MWRs of the altimeters. The ZWD of the PGSs were compared with the radiosonde-derived data in the regional and sub-regional solutions. The difference between them was −7.72~2.79 mm with an RMS of 14.53~18.62 mm, which showed good consistency between the two. Then, the PGSs’ ZWD was used to validate the MWRs. To reduce the land contamination of the MWR, we determined validation distances of 6~30 km, 16~28 km, and 18~30 km for Jason-3, HY-2B, and HY-2C, respectively. The ZWD differences between PGSs and the Jason-3, HY-2B, and HY-2C altimeters were −2.30 ± 16.13 mm, 9.22 ± 22.73 mm, and −3.02 ± 22.07 mm, respectively.

<strong class="journal-contentHeaderColor">Abstract.</strong> Global Navigation Satellite Systems are well-known fundamental tools for crustal monitoring projects and tectonic studies, thanks to the high coverage and the high-quality data. In slow convergent margins, in particular, where the deformation rates are of the order of few mm/yr, the GNSS monitoring is beneficial to detect the diffused deformation which is responsible for the tectonic stress accrual. Its strength is the high precision reached by GNSS permanent stations, particularly if long span-data and stable monuments are available at all the stations. North-East Italy is a region which can take the most from continuous and high-precision geodetic monitoring, since it is a tectonically active region located in the northernmost sector of the Adria microplate, slowly converging with the Eurasia plate, but characterised by low deformation rates and moderate seismicity. Furthermore, this region is equipped with a permanent GNSS network providing real-time data and daily observations over two decades. The Friuli Venezia Giulia Deformation Network (FReDNet) was established in the area in 2002 to monitor crustal deformation and contribute to the regional seismic hazard assessment. This paper describes GNSS time series spanning two decades of stations located in the NE-Italy and surroundings, as well as the outcoming velocity field. The documented dataset has been retrieved by processing the GNSS observations with the GAMIT/GLOBK software ver10.71, which allows calculating high-precision coordinate time series, position and velocity for each GNSS station, and by taking advantage of the high-performance computing resources of the Italian High-Performance Computing Centre (CINECA) clusters. The GNSS observations (raw and standard RINEX formats) and the time series estimated with the same procedure are currently daily continued, collected and stored in the framework of a long-term monitoring project. Instead, velocity solutions are planned to be updated annually. The time series and velocity field dataset documented here is available at <a href="https://doi.org/10.13120/b6aj-2s32" target="_blank" rel="noopener">https://doi.org/10.13120/b6aj-2s32</a> (Tunini et al., 2023).

From October 7, 2012, to January 28, 2017 (GPS weeks 1709–1933) all products of the International GNSS Service (IGS)—precise ephemerides of GPS and GLONASS satellites, coordinates and velocities of permanent GNSS stations, etc.—were based on the IGb08 reference frame, the updated IGS realization of the release of the International Terrestrial Reference Frame (ITRF2008). Observations of GNSS satellites at permanent stations located in Ukraine and in Eastern Europe for this period were processed in the GNSS Data Analysis Centre of the Main Astronomical Observatory of the National Academy of Sciences of Ukraine. The processing was carried out with Bernese GNSS Software ver. 5.2 according to the requirements of the European Permanent GNSS Network (EPN) that were valid at that time. In total, observations on 232 GNSS stations, including 201 Ukrainian stations belonging to the following operators of GNSS networks were processed: MAO NAS of Ukraine, Research Institute of Geodesy and Cartography, NU Lviv Polytechnic (GeoTerrace), PJSC System Solutions (System.NET), TNT TPI company (TNT TPI GNSS Network), Navigation Geodetic Center Ltd. (NGC.net), UA-EUPOS/ZAKPOS, Europromservice Ltd. (EPS), Coordinate Navigation Maintenance System of Ukraine (CNMSU), and Kharkiv National University of Radio Electronics. The IGb08 reference frame was set by No-Net-Translation conditions on the coordinates of the IGS Reference Frame stations. As result, the stations’ coordinates in the IGb08 reference frame and the zenith tropospheric delays for all stations were estimated. The mean repeatabilities for components of stations’ coordinates for all weeks (the characteristics of the precision of the received daily and weekly solutions) are in the following ranges: for north and east components—from 0.5 mm to 1.6 mm (average values are 0.99 mm and 1.01 mm respectively), for hight component—from 2.2 mm to 5.4 mm (average value is 3.75 mm) with an outlier of 6.91 mm for GPS week 1759.

The second reprocessing campaign of historical observations of GNSS satellites at permanent stations located in Ukraine and in the Eastern Europe for GPS weeks 935–1708 (December 7, 1997 – October 6, 2012) was carried out in the GNSS Data Analysis Centre of the Main Astronomical Observatory NAS of Ukraine with using products updated in IGS repro2 and EPN-Repro2 campaigns – precise ephemerides of GPS and GLONASS satellites, coordinates and velocities of reference permanent GNSS stations, etc. The observations was analyzed with the Bernese GNSS Software ver. 5.2 according to the requirements of the EUREF Permanent GNSS Network (EPN), that were valid at that time. In total, observations on 72 GNSS stations, including 48 Ukrainian stations belonging to the following operators of GNSS networks: MAO NAS of Ukraine, Research Institute of Geodesy and Cartography, TNT TPI company (TNT TPI GNSS Network), PJSC System Solutions (System.NET), Lviv Polytechnic National University, UNAVCO, Inc. (USA), were processed. The IGb08 reference frame was realized by applying No-Net-Translation conditions on the coordinates of the IGS Reference Frame stations. As result, the stations’ coordinates in the IGb08 reference frame and the zenith tropospheric delays for all stations were estimated. The mean repeatabilities for components of stations’ coordinates for all weeks (the characteristics of the precision of the received daily and weekly solutions) are in the following ranges: for north and east components – from 0.6 mm to 1.6 mm (average values are 1.02 mm and 0.94 mm respectively), for height component – from 2.2 mm to 5.2 mm (average value is 3.36 mm) with the outlier of 5.79 mm for GPS week 943. The coordinates of the permanent GNSS stations for one weekly solution are presented.

The second reprocessing campaign of historical observations of GNSS satellites at permanent stations located in Ukraine and in the Eastern Europe for GPS weeks 935–1708 (December 7, 1997 – October 6, 2012) was carried out in the GNSS Data Analysis Centre of the Main Astronomical Observatory NAS of Ukraine with using products updated in IGS repro2 and EPN-Repro2 campaigns – precise ephemerides of GPS and GLONASS satellites, coordinates and velocities of reference permanent GNSS stations, etc. The observations was analyzed with the Bernese GNSS Software ver. 5.2 according to the requirements of the EUREF Permanent GNSS Network (EPN), that were valid at that time. In total, observations on 72 GNSS stations, including 48 Ukrainian stations belonging to the following operators of GNSS networks: MAO NAS of Ukraine, Research Institute of Geodesy and Cartography, TNT TPI company (TNT TPI GNSS Network), PJSC System Solutions (System.NET), Lviv Polytechnic National University, UNAVCO, Inc. (USA), were processed. The IGb08 reference frame was realized by applying No-Net-Translation conditions on the coordinates of the IGS Reference Frame stations. As result, the stations’ coordinates in the IGb08 reference frame and the zenith tropospheric delays for all stations were estimated. The mean repeatabilities for components of stations’ coordinates for all weeks (the characteristics of the precision of the received daily and weekly solutions) are in the following ranges: for north and east components – from 0.6 mm to 1.6 mm (average values are 1.02 mm and 0.94 mm respectively), for height component – from 2.2 mm to 5.2 mm (average value is 3.36 mm) with the outlier of 5.79 mm for GPS week 943. The coordinates of the permanent GNSS stations for one weekly solution are presented.

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract: &amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;amp;#160;&amp;lt;/strong&amp;gt;The increasing development of GNSS techniques enables solving geodetic problems on both local and global scales. Parallelly, complex algorithms have been proposed and can also be solved well by Machine Learning (ML). However, ML techniques are sometimes not sensitive enough to gain results with a high probability for some cases, like sparse data or non-stationary GNSS time series. In this study, we use a combination of Human and Machine learning (H&amp;amp;M) to improve the classification performance of continuous GNSS stations. First, 427 permanent GNSS stations are obtained from the EUREF network to train ML models. The models are then applied to classify the quality of 939 continuous observation stations from two projects, EIFEL and IPOC, carried out by the German Research Centre for Geosciences (GFZ), Potsdam, Germany. Next, we independently validate the ML models' reality through a MATLAB program, GNSS metadata, and seismic data. Finally, all data of these 1366 stations are used to re-train the ML models. The main criteria to classify are the number of outliers, jumps in GNSS time series, root mean square errors, observation time-spans, and stability of the crustal motion velocity fields. Applying the approach of the H&amp;amp;M combination improves the performance of the ML models up to 92% while using only ML methods remains ~68%. These ML-based classification models can be applied to estimate the quality of permanent GNSS stations and to manage big databases. The result is the basis for selecting suitable control and monitoring stations in crustal deformation monitoring as well as in civil and industrial applications.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords: &amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;GNSS station classification, Machine learning, Human &amp;amp; Machine learning combination.&amp;lt;/p&amp;gt;

The relevance of PPP technology has greatly increased over the past 10 years. While the centimeter level of accuracy has already been practically ensured in most spheres of economic activity, first of all, in geodesy, the millimeter level for scientific tasks related to the study of the influence of geophysical factors on the environment is still the subject of research. The purpose of this work is to identify the real accuracy of the modification of the PPP-AR method using the example of data from four permanent GNSS stations: SULP (Lviv), FRAN (Ivano-Frankivsk), RAHI (Rakhiv), TERN (Ternopil), included in the European the EPN network (EUREF Permanent GNSS Network). Method. To fulfill the given task, we used data from four permanent GNSS stations. 3 weeks of observations were processed: one in July (2217 GPS week), one in August (2222 GPS week) and one in September (2226 GPS week). Processing of the observation files was carried out in the PRIDE PPP-AR software environment, which was pre-installed on the server with the Ubuntu operating system. The coordinates of each station were calculated with the same input parameters, namely, taking into account the second-order ionospheric correction and the function of displaying inclined zenith delays of signals from satellites in the direction of the VMF3 zenith. We compared the obtained sets of coordinates with the control coordinates of these stations. For the time periods we chose, the coordinates calculated by the relative method based on the formation of phase differences in the combined EPN processing center were taken as control. The results. The main results of our research were the difference of coordinates (comparison of the received coordinates with control ones). For each station, the obtained differences are unidirectional in nature and vary little between stations. The average value of the coordinate differences was from 0.6 to 7.2 mm and practically does not depend on the processing time interval. The root mean square error (RMS) of the coordinate differences is at the level of 1.5 – 3 mm and also changes little over time. It was found that the accuracy of determining the coordinates based on the processing of GNSS measurement data at permanent stations SULP, TERN, FRAN, RAHI using the PPP-AR method is quite high, but a systematic difference of several millimeters is noticeable, which may be caused by insufficient consideration of some factors of geophysical origin . Scientific novelty and practical significance. It is shown that the PPP-AR method at the current stage of development of GNSS technologies achieves the accuracy of the coordinate determination method based on phase differences and can be applied not only in geodesy tasks, but also in geodynamic studies, provided the results of daily GNSS observations are used. A promising direction for further research is the identification of unaccounted sources of systematic errors.

Time series of weekly and daily solutions for coordinates of permanent GNSS stations may indicate local deformations in Earth’s crust or local seasonal changes in the atmosphere and hydrosphere. The errors of the determined changes are relatively large, frequently at the level of the signal. Satellite radar interferometry and especially Persistent Scatterer Interferometry (PSI) is a method of a very high accuracy. Its weakness is a relative nature of measurements as well as accumulation of errors which may occur in the case of PSI processing of large areas. It is thus beneficial to confront the results of PSI measurements with those from other techniques, such as GNSS and precise levelling. PSI and GNSS results were jointly processed recreating the history of surface deformation of the area of Warsaw metropolitan with the use of radar images from Envisat and Cosmo-SkyMed satellites. GNSS data from Borowa Gora and Jozefoslaw observatories as well as from WAT1 and CBKA permanent GNSS stations were used to validate the obtained results. Observations from 2000–2015 were processed with the Bernese v.5.0 software. Relative height changes between the GNSS stations were determined from GNSS data and relative height changes between the persistent scatterers located on the objects with GNSS stations were determined from the interferometric results. The consistency of results of the two methods was 3 to 4 times better than the theoretical accuracy of each. The joint use of both methods allows to extract a very small height change below the level of measurement error.

This paper applies time-frequency analysis to a 3-day time series with a sampling interval of 1 second of the changes in E, N and H coordinates of three permanent GNSS stations: WRON, KR10, and KRUR in Krakow, as well as differences between them. Time-frequency analysis was conducted using a Fourier transform band-pass filter, which separates time series into frequency components. By analyzing the differences between these coordinates, it was observed that the WRON station shows a systematic error in the form of a regular wideband oscillation with a period of 75 minutes, whose amplitude varies from approximately 1 to 3 mm with a period of about 1 day. In the horizontal plane, this oscillation takes the shape of a ˚attened ellipse with a semi-major axis oriented in the northwest direction. The most probable cause of this regular oscillation is the day-to-day variability of the multipath signal environment.

&amp;lt;p&amp;gt;Permanent GNSS stations have become fundamental for geodynamic studies thanks to their capability of providing consistent coordinate time series. The time series analysis is becoming more and more sophisticated and there are several approaches, fully automated or not, helping the users to derive the main parameters of interest such as: trends, periodical signals, discontinuities, types of noises, blunders. Typically, however, the analysis of the time series is still performed considering separately each of the three coordinate components. Actually, this neglects the three-dimensional nature of the GNSS position solutions, which are computed simultaneously, and may have some impact on the analysis. We should also bear in mind that the values of the coordinates time series depend on the reference system orientation. For instance, the time series values expressed in geocentric coordinates (X, Y, Z) are usually different from the same ones represented in a topocentric (E, N, V) reference. Therefore, if the analysis is performed separately on the three coordinate components, results will be different depending on the adopted reference system.&amp;lt;br&amp;gt;The aim of this work is to address the issue concerning the automated rejection of outliers potentially present in the GNSS time series. This is a fundamental aspect considering the large amount of data that nowadays shall be continuously processed and analyzed, thus requiring procedures as automated as possible. A viable approach is to search for outliers by analyzing the error distribution of the coordinates after having removed trends and signals, assuming that these behave like casual errors and follow a normal density distribution. It is then possible to set a statistical threshold in order to reject iteratively all the solutions with higher residual values. This approach is usually implemented by considering mono-dimensional time series in which the three coordinate components are processed separately. Nevertheless, from a statistical point of view, each GNSS position solution should be considered to be a 3D variable, thus characterized by a probability density function defined in a 3D space. In particular, by considering a chi-square distribution with three degrees of freedom it is possible to consider an ellipsoidal density function that well fit the error distribution of a 3D casual variable such as the GNSS coordinates.&amp;lt;br&amp;gt;In this work, numerical results obtained from the analysis of real dataset will be presented. In particular, six years of daily position solutions obtained from 12 GNSS permanent stations have been considered. The time series have been analyzed starting from both geocentric and topocentric coordinates using alternatively two different approaches: a classical one, in which the three coordinate components have been processed separately, and the 3D approach that allowed to consider the three coordinates at once. Results show that the second approach is mostly independent from the starting reference system, whereas the classical approach is affected by the orientation of the Cartesian axes used to project the same positions.&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;The release of Android GNSS Raw Measurements API, (2016) and the growing technological development introduced by the use of multi-GNSS and multi-frequency GNSS chipsets &amp;amp;#8211; changed the hierarchies within the GNSS mass-market world. In this sense, Android smartphones became the new leading products. Positioning performances and quality of raw GNSS measurements have been studied extensively. Despite the greater susceptibility to multipath and cycle slip due to the low cost antenna used, a positioning up to sub-meter accuracy can be achieved. Among the improvements in positioning and navigation, the availability of GNSS measurements from Android smartphones paved new ways in geophysical applications: e.g. periodic fast movements reconstruction and ionospheric perturbances detection. &amp;amp;#160;In fact, considering the number of Android smartphones compatible with the Google API, additional costless information can be used to densify the actual networks of GNSS permanent stations used to monitor atmospheric conditions. However, an extensively analysis on the reconstruction of ionospheric conditions with Android raw measurements is necessary to prove the accuracy achievable in future ionosphere monitoring networks based on both permanent GNSS station and Android smartphone.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The aim of this work is to assess the performance of multi-frequency and multi-GNSS smartphone &amp;amp;#8211; in particular, Xiaomi Mi 8 and Huawei Mate 20 X &amp;amp;#8211; in the reconstruction of real-time sTEC (slant Total Electron Content) variations meaningful of ionospheric perturbations. A 24-hour dataset of 1Hz GNSS measurements in static conditions was collected from the two smartphones in addition to data collected from M0SE, one of the EUREF/IGS permanent stations. The VARION (Variometric Approach for Real-time Ionosphere Observations) algorithm, based on the variometric approach and developed within the Geodesy and Geomatics Division of Sapienza University of Rome, was used to retrieve sTEC variations for all the observation periods.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results, although preliminary, show that it is possible to study also from the smarthphone the trend of sTEC variations with elevation: lower elevation angles cause noisier sTEC variations. RMSE of the order of 0.02 TECU/s are found for elevation angles higher than 20 degrees as it happens for permanent stations. At the same time, the sTEC variations were compared to the overall measurements noise, due to both environmental and receiver noise, in order to statistically define the correlation between RMSE and derived sTEC variation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Although the results obtained in this work are encouraging, still further analyses need to be carried out especially at latitudes where ionosphere conditions and perturbations play a major role. However, the possibility to perform such analyses on datasets collected worldwide is prevented from their availability. The last part of this work is therefore focused on the identification of a methodology to share with the GNSS community to collect, store and share GNSS measurements from Android smartphones to enable the researchers to enlarge the spatial and temporal boundaries of their research in the field of ionosphere modelling with mass-market devices.&amp;amp;#160;&amp;lt;/p&amp;gt;