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342 results for “GNSS”
GNSS Vertical Crustal Displacement Data around Qinghai Lake
<p>This dataset includes vertical crustal displacement observations from 13 GNSS stations near Qinghai Lake, spanning from 2016 to 2023. The data consists of GNSS vertical displacement time series (GNSS_Upper.mat, MATLAB format), geographic coordinates of the stations (site_info.txt), and the corresponding time vector (GNSS_datatime.mat). </p> <p>This dataset supports the study described in the article "Terrestrial water storage changes of Qinghai Lake on the Tibetan Plateau from joint inversion of GNSS and InSAR data" (currently under peer review). The article combines GNSS and InSAR data to investigate vertical deformation field and terrestrial water storage changes in the Qinghai Lake region on the Tibetan Plateau. </p>
Heights of 22000+ GNSS stations on the surface from Nevada Geodetic Laboratory
<p>This dataset is a point dataset of height of 22000+ GNSS around the world. This dataset is used to validate GDEMM2024 dataset from <a href="https://www.nature.com/articles/s41597-024-03920-x#Sec3">E. Sinem Ince, Oleh Abrykosov and </a><a href="https://www.nature.com/articles/s41597-024-03920-x#Sec3">Christoph Förste (2024)</a>, and have a potential to validate land digital elevation model or serve as ground control points (GCPs) for georeference and statellite images correction. </p> <p><strong>Data process</strong></p> <p>Data are retrieved from the Nevada Geodetic Laboratory (NGL) (<a href="http://geodesy.unr.edu/index.php">http://geodesy.unr.edu/index.php</a>) as a .csv file, and we converted it to the geolocation in EPSG:4326</p> <p><strong>Files description</strong></p> <ul> <li>raw data from NGL website:<em><strong> goint_point.csv</strong></em></li> <li>altitude of gps station height<strong><em>: altitude_gnss_global_v2.geojson</em></strong></li> <li>juypternotebook to convert stations' height: <strong><em>generate_gnss_points.ipynb</em></strong></li> </ul> <p> </p> <p> </p>
Sichuan GNSS data and earthquake catalog
<p>The GNSS data is provided by GNSS data product service platform of China Earthquake Administration. </p>
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
Line-Source Model based Rapid Inversion for Deriving Large Earthquake Rupture Characteristics using High-rate GNSS Observations
<p>The high-rate GNSS data and GNSS-derived velocity waveforms of six large earthquakes (the 2016 Mw 6.6 Norcia earthquake, the 2010 Mw 7.2 EI Mayor-Cucapah earthquake, the 2016 Mw 7.8 Kaikoura earthquake, the 2019 Mw 7.1 Ridgecrest earthquake, the 2014 Mw 8.2 Iquique earthquake, and the 2015 Mw 8.3 Illapel earthquake) are included in this repository.</p>
GNSS and levelling data to detect ground deformation along the Upper Adriatic Sea coastal area (Italy)
<p>This geodetic dataset includes both Global Navigation Satellite System (GNSS) and levelling data. GNSS measurements were recorded by continuous stations managed by public institutions and private companies, while levelling measurements were obtained by the use of benchmarks managed by ENI S.p.A. </p> <p>This dataset is used in the manuscript entitled "Multi-technique geodetic detection of onshore and offshore subsidence along the Upper Adriatic Sea coasts" to estimate deformation around the littoral area of Ravenna (Italy) (Polcari et al., 2022). The GNSS data, from permanent stations RAVE, PCTA, FIUN and ANGA covers the period from around 1998 to 2018. The files in .csv format contain displacement time series with respect to the Adria-fixed reference frame and for PCTA, FIUN and ANGA also with respect to RAVE GNSS station.</p> <p>The levelling data refer to campaigns that took place in 2002, 2003, 2004, 2005, 2007, 2009, 2011, 2014, and 2017. The file named <em>Original.csv</em> contains the original height measurements for each benchmark, while the file named <em>Ref.RAVE.csv</em> contains the mean velocity and the displacement calculated for all 147 benchmarks. In this last file the data were scaled with respect to the mean velocity of the benchmark located near the RAVE station.</p>
High Rate GNSS Velocities for Earthquake Strong Motion Signals
<p>This zipped dataset consists of directories by earthquake of 5Hz GNSS velocity time series.</p> <p>── comcat_earthquake_code</p> <p>└── velocities_4char_DOY_YYYY.txt</p> <p> </p> <p>References:</p> <ul> <li><a href="https://earthquake.usgs.gov/data/comcat/">USGS Comcat Catalog</a> </li> <li><a href="https://www.unavco.org/data/gps-gnss/gps-gnss.html">UNAVCO Archive 4char station codes</a></li> <li><a href="http:// https://github.com/crowellbw/SNIVEL">SNIVEL processing software repo</a></li> </ul>
Kamchatka 2013 GNSS Time series
<p>GNSS time series presented in the article: "The 2013 slab-wide Kamchatka earthquake sequence"</p> <p>The columns of the files correspond to </p> <p>Year ; Month ; Day ; Hour ; Minute ; Second ; East position (mm) ; North position (mm) ; Up position (mm) ; East uncertainty (mm) ; North uncertainty (mm) ; Up uncertainty (mm) ;</p>
MCMC results for GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI"
<p>Contains the results of "<a href="https://doi.org/10.5281/zenodo.6825238">GARPOS-MCMC v1.0.0</a>" for the GNSS-A data publishied by the Japan Coast Guard (<a href="https://doi.org/10.5281/zenodo.6417480">https://doi.org/10.5281/zenodo.6417480</a>). <br>Please refer Watanabe et al. (2023, J. Geod., <a href="https://doi.org/10.1007/s00190-023-01774-6">https://doi.org/10.1007/s00190-023-01774-6</a>) for the details for methods and figure captions.</p> <p>Contents:</p> <p><Direcotry name><br>- *01-DoubleGrad: Results obtained for the m100 model<br>- *02-SingleGrad: Results obtained for the m101 model<br>- *03-Alpha2Offset: Results obtained for the m102 model<br>- x-alpha_correlation: Figures of artificially obtained correlation between the seafloor position and the sound speed perturbations. </p> <p>1. MCMC figures</p> <p><File description><br>- *-cahin-1.png: Series of MCMC samples (all)<br>- *-histogram.png: Histogram and distribution of MCMC samples</p> <p><Files><br>fig01-DoubleGrad.tar.gz<br> - fig01-DoubleGrad<br> - [SITE]<br> - *-cahin-1.png<br> - *-histogram.png</p> <p>fig02-SingleGrad.tar.gz<br> - fig02-SingleGrad<br> - [SITE]<br> - same as fig01</p> <p>fig03-Alpha2Offset.tar.gz<br> - fig03-Alpha2Offset<br> - [SITE]<br> - same as fig01</p> <p>x-alpha_correlation.tar.gz<br> - [SITE]*-corrXA.png</p> <p>2. Result data</p> <p><File description><br>- *-cahin.csv: All MCMC samples<br>- *-hp.csv: Statistical information (percentiles) for hyperparameters<br>- *-m.p.dat: Model parameter for MAP sample<br>- *-obs.csv: Data and residuals for MAP sample<br>- *-res.dat: Position solution for MAP sample<br>- *t.s*.png: Residuals and perturbation model for MAP sample</p> <p><Files><br>res01-DoubleGrad.tar.gz<br> - res01-DoubleGrad<br> - [SITE]<br> - *-cahin.csv<br> - *-hp.csv<br> - *-m.p.dat<br> - *-obs.csv<br> - *-res.dat<br> - fig<br> - *t.s*.png</p> <p>res02-SingleGrad.tar.gz<br> - res02-SingleGrad<br> - [SITE]<br> - same as res01</p> <p>res03-Alpha2Offset.tar.gz<br> - res03-Alpha2Offset<br> - [SITE]<br> - same as res01</p>
GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI" for the identical four transponder-array
<p>Contains the GNSS-A data obtained by the Japan Coast Guard at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI" for the identical four transponder-array.</p> <p>The datasets are stored in the following directories and files for analyses after array determination:<br> -[initcfg]: contains initial site-parameter files<br> -[obsdata]: contains initial acoustic observation files<br> -[single-default]: contains the prep. results (without array-geometry constraint)<br> -[cfgfix]: contains site-parameter files for array-geometry constraint analysis<br> -[fix4sta-abic]: contains the empirical Bayes' results obtained with array-geometry constraint</p> <p>These data can be/were processed with <a href="https://doi.org/10.5281/zenodo.6414642">GARPOS v1.0.1</a>, <a href="https://doi.org/10.5281/zenodo.6825238">GARPOS-MCMC v1.0.0</a>.</p>
GNSS and Digisonde sporadic-E maps produced by Air Force Institute of Technology
<p>Spatial maps of sporadic-E are produced by combining COSMIC-2 RO data with Digisonde measurements, using the <em>S<sub>4</sub></em>-based approach described by Carmona et al. (2022). Images from the TEC-based approach used in other studies are also included for reference. </p> <p> </p> <p>Carmona, R. A., Nava, O. A., Dao, E. V., & Emmons, D. J. (2022). A Comparison of Sporadic-E Occurrence Rates Using GPS Radio Occultation and Ionosonde Measurements. <em>Remote Sensing</em>, <em>14</em>(3), 581.</p> <p> </p>
GNSS data from Irkutsk stations during the 2015 June 22
<p>Raw data from 2 GNSS stations in Irkutsk, Russia during the 2015 June 22. Irk_15JUN22_000000.rar contains S4, phase scintillations records; ISTP_2015_06_22_00.00.00.jps contains L1, L2, P1, P2 records. We upload GPStation-6 and Javad Delta-G3T data. It is raw data in .gps (https://www.novatel.com/assets/Documents/Manuals/om-20000132.pdf) and .GREIS (http://www.javad.com/jgnss/support/manuals.html) formats.</p>
The raw position time series of 4 continous GNSS sites (YAAN, QLAI, CHDU, PIXI) and the final GNSS velocity solution of the Chengdu-Chongqing economic area (CCEA)
Open the record for dataset details and reuse information.
GNSS interferometric reflectometry data for Silveira et al. 2024
<p>Supporting data for </p> <p>L. Silveira, V.H. Almeida Jr., M.K. Yamawaki, E. Puhl, R. Manica, E. Toldo Jr., T. Silva, F. Geremia-Nievinski (2024), Wide-swath satellite altimetry reveals the 2024 Porto Alegre extreme flood was intensified by backwater effect across choked river section, submitted.</p>
High-rate GNSS displacement waveforms for large earthquakes version 2.0
<p>This dataset accompanies <strong>A Global Database of Strong Motion Displacement GNSS Recordings and an Example Application to PGD Scaling</strong> published in <em>Seismological Research Letters</em> by Ruhl et al. (2018). The data is structured as follows:</p> <p>Once expanded the data within the archive are structured as follows: Inside the archive there is one folder per event clearly labeled with the event names in Table 1 from the main text. Inside each event folder is a text file (EVENT_disp.chan) with station metadata (station codes, coordinates, and gain values), importantly because the waveforms are provided as mini-SEED files with integer values the gain must be applied to convert to physical displacement units. There is a “disp” folder which contains files named using the convention STA.LXE.mseed, STA.LXN.mseed and STA.LXZ.mseed where “STA” is the station code and LXE, LXN, and LXZ are east, north, and up waveforms, respectively. The sampling rate for each waveform is indicated inside the miniSEED header of each waveform as well as in the corresponding metadata channel file. The data are provided in UTC time with leap seconds fully corrected for so no further processing is necessary. There is also a "_plots" folder with a plot of the three-component record section for each event.</p>
Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS - Supporting Data
<p>Processed data used in the paper Smittarello et al., JGR 2019</p> <p>Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS</p> <p>e.g. GNSS, seismic and InSAR data</p>
Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes
<p>Post-processed and real-time GNSS displacement waveforms for the 2019 Ridgecrest earthquakes. Waveforms are for the M6.4 and M7.1 events recorded at 1 and 5Hz sample rates. Data are in miniSEED format, channel codes LYE, LYN, LYZ correspond to east, north, and up respectively. Displacement units are meters and time is UTC. The data are trimmed 60s before the USGS origin times for<br> the earthquakes. No filtering has been applied.</p> <p>A paper describing the data has been submitted to SRL. In the meantime if you use the data please cite our <a href="https://eartharxiv.org/pdxqw/">preprint on the EarthArXiv</a> as:</p> <p>Melgar, D., TI Melbourne, BW Crowell, J Geng, W Szeliga, C Scrivner, M Santillan, DER Goldberg. 2019. Real-time High-rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes. EarthArXiv, doi:10.31223/osf.io/pdxqw.</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
GNSS Ground Monitoring Data of the Hongyanzi Landslide Area in China from November 11, 2021 to June 4, 2022
<p>This dataset comprises Global Navigation Satellite System (GNSS) ground monitoring data collected from five distinct locations within the Hongyanzi landslide area in China. The data spans a critical time period from November 11, 2021, to June 4, 2022, providing valuable insights into the temporal deformation behavior of the landslide.</p> <p>To enhance the applicability and comparability of these GNSS measurements, we have processed the raw three-dimensional (3D) displacement vectors and projected them onto the Line-Of-Sight (LOS) direction of Interferometric Synthetic Aperture Radar (InSAR) observations. This transformation facilitates direct comparisons between the GNSS-derived deformation rates and those derived from InSAR data, enabling a more comprehensive understanding of the landslide dynamics.</p> <p>The dataset includes:</p> <ul> <li><strong>Time series of 1D displacement measurements</strong> along the InSAR LOS direction for each of the five GNSS stations.</li> </ul> <p>The high-resolution temporal coverage of this dataset, coupled with the transformation to the InSAR LOS direction, makes it an invaluable resource for researchers investigating the geomechanics of the Hongyanzi landslide, as well as for validation and calibration of remote sensing-based deformation monitoring techniques.</p> <p><strong>Potential Uses</strong>:</p> <ul> <li>Validation of InSAR-derived deformation maps and velocity fields.</li> <li>Study of landslide kinematics and triggering mechanisms.</li> <li>Integration with other geophysical and geological datasets for comprehensive hazard assessments.</li> <li>Development and testing of advanced deformation monitoring and early warning systems.</li> </ul> <p><strong>Access and Citation</strong>:<br>This dataset is freely available for research purposes under the <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener noreferrer">Creative Commons Attribution 4.0 International License</a>. Please cite this dataset using the following reference (to be filled in upon upload completion):</p> <p>Aoqing Guo, 2024. GNSS Ground Monitoring Data for the Hongyanzi Landslide Area in China: Transformed to InSAR LOS Direction (November 11, 2021 - June 4, 2022). Zenodo. 10.5281/zenodo.13254357.</p>
GNSS data collected with low-cost antennas
<p>The dataset contains GNSS data collected during ten days. To isolate the antenna-related errors from atmospheric propagation ones, an ultra-short baseline was used. A setup consisting of three types of low-cost antennas that support GPS, Galileo, GLONASS, and BeiDou systems measurements and gain<span> </span>> 25 dB was selected for the experiment. In addition, two surveying and geodetic grades Trimble antennas, provide benchmark performance results. The session duration at both mounting points and each measuring day was 16 hours, from 8:00:00 to 24:00:00 UTC. An interval of 5 s and an elevation mask of 0° were adopted.</p>
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