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342 results for “gnss”
GNSS stations along the Himalayan plate boundary
<p><span>GNSS stations from several sources are considered in the present study surrounding the Himalayan plate boundary. The horizontal geodetic velocities in ITRF 2014 are transformed with respect</span><span> </span><span>to the fixed India reference frame.</span></p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Gummern - Horizontal (2D) vertical (1D) displacements from low cost GNSS
<h2>Abstract</h2> <p>Horizontal (2D) and vertical(1D) displacements, estimated for two locations in Gummern, AUT.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Gummern (horizontal-2D and vertical-1D displacements)</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Horizontal (2D) and vertical(1D) displacements, estimated for two locations in Gummern, AUT.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>GNSS, displacements, coordinate time series</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>GNSS</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>14.06.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>14.06.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>None</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>5 mm</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4883</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Veton Hamza (<a href="mailto:veton.hamza@fgg.uni-lj.si">veton.hamza@fgg.uni-lj.si</a>)</p> <p>Polona Pavlovcic Preseren (<a href="mailto:polona.pavlovcic-preseren@fgg.uni-lj.si">polona.pavlovcic-preseren@fgg.uni-lj.si</a> )</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Veton Hamza (<a href="mailto:veton.hamza@fgg.uni-lj.si">veton.hamza@fgg.uni-lj.si</a>)</p> <p>Polona Pavlovcic Preseren (<a href="mailto:polona.pavlovcic-preseren@fgg.uni-lj.si">polona.pavlovcic-preseren@fgg.uni-lj.si</a> )</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
GNSS amplitude scintillation index (S4) and ionospheric pierce point (IPP) from Zhuhai on 11 May 2024
<p>The ionospheric pierce point (IPP) location is named as "IPP_lla_CXX", sampled by 1-sec (86400 a day), where the columns are latitude, longitude, and altitude, respectively.</p> <p>The amplitude scintillation index (S4) is named as "S4_CXX_SYSU_132", sampled by 1-minute cadence (1440 a day).</p> <p>The data are provided in MATLAB format.</p>
GNSS data at the Astrolabe Glacier
<p>This dataset contains GNSS measurements collected at the Astrolabe Glacier (Terre Adélie, East Antarctica) from January 17, 2023, to February 2, 2023 and presented in:</p> <p>Le Bris et al. (2024), <span>Spatial and Temporal Variability in Tide-induced Icequake Activity at the Astrolabe Coastal Glacier, East Antarctica. <em>Submitted in JGR.</em><br></span></p> <p>Data from each GNSS station (7 stations in total) are saved in individual files. The processed data (*pos files) are organized by columns as follows: Time, North, East, Up, and Up Detrended. Additionally, raw GNSS data files used to generate the processed outputs are included. Details on data processing steps are provided in the supporting materials of the above reference.</p>
Real-time GNSS troposphere products: ZTD and gradients - hurricane Lorenzo case study
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute for a subset of EPN stations during two test periods (21 days long each):</p> <p>1) Day of year (DoY) 201 - 221, 2019</p> <p>2) DoY 266 - 286, 2019.</p> <p>The processing strategy can be found in https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration.</p> <p>For convenience, products are stored in 2 formats (but each contains identical data):</p> <p>1. Self-explanatory netCDF format (https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html), which follows the parameter naming convention of the troposphere SINEX v2 format: https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf. Each file contains data for all stations but only for one test period.</p> <p>2. Semicolon delimited text files, with a self-explanatory header line. Each file contains daily products for one station.</p>
Data for the research article: "Detecting Seismo-ionospheric Anomalies Possibly Associated with the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES and Swarm Observations"
<p>The zip archives contain 10 .mat files (MATLAB readable). Each .mat file can be loaded into the MATLAB workspace using the <em>load</em> command.</p>
GNSS-A data observed in SAGA site in SGO-A using UAV (November 2022)
<p>Contains the GNSS-A data obtained by UAV at SAGA site in SGO-A on November 2022.</p> <p>Initial datasets are stored in the following directories:<br> -[initcfg]: contains initial site-parameter files<br> -[obsdata]: contains initial acoustic observation files</p> <p>Datasets after array determination are stored in the following directories:<br> -[single-default]: contains the results of preprocessing (non-rigid-array analysis)<br> -[cfgfix-default]: contains initial site-parameter files for rigid-array analysis<br> -[fix-abic]: contains the results of preprocessing (rigid-array analysis)</p> <p>These data can be processed with <a href="https://doi.org/10.5281/zenodo.4522027">GARPOS</a>.</p> <p><a href="https://www1.kaiho.mlit.go.jp/KOHO/chikaku/kaitei/sgs/datalist_e.html">SGO-A</a> has been operated by the Japan Coast Guard.</p>
GNSS position time series (.neu files) and seismic velocity strcture (Profil_lat2822_Vp_Vs.dat) used in the manuscript
<p>These are the raw GNSS time series files (in .neu format) and seismic velocity strcture file (Profil_lat2822_Vp_Vs.dat) we used in the manuscript. These data are not allowed to use before the manuscript is accepted.</p>
High-rate GNSS Raw Doppler Positive Impact on Cascading Filter-based Approach for Improving Real-time Transient Coseismic Velocities Modeling
<p>The high-rate GNSS average and instantaneous coseismic velocity waveforms for the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku earthquake are included in this repository.</p>
GNSS-Acoustic Data Capturing the Mw 8.2 July 28, 2021 Chignik Earthquake
<p>GNSS-Acoustic surveys collected at a site, SEM1, along the Alaska Subduction Zone southwest of Kodiak, AK from 2018-2021. These data contain positions close to the trench of the subduction zone both prior to and following the July 28, 2021 Mw 8.2 Chignik earthquake. Contains raw data files, data extractions in a text column format, and processed site positions.</p>
Retrieving accurate precipitable water vapor based on GNSS multi-antenna PPP with an ocean-based dynamic experiment
<p>GNSS-derived PWVs during a 4-day shipborne experiment are included in this repository. There are four solutions with four different processing strategies, namely PPP without constraints (Conventional), with baseline length constraint only (Bl), with common ZTD constraint only (Com), and with both baseline length and common ZTD constraints (Bl+Com). The suffix 'Smooth' in the file names represents the PWV results after backward smoothing.</p>
The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS
<p>Companion data set to the paper "<strong>The Impact of the </strong><strong>Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</strong>" The 'GNSS_Stations' folder contains the GNSS stations used for 2011 <strong>M</strong>7.9 Ibaraki, 2011 <strong>M</strong>7.4 Iwate, 2011A <strong>M</strong>7.3 Miyagi and 2003 <strong>M</strong>8.3 Tokachi 2003 earthquakes. The 'Mesh' folder contains the fault geometry mesh files for the Japan Trench, including the GMSH files. The '3D_velocity_model' folder contains the 3D velocity model for the combined West and East domains of the 3D Japan Integrated Velocity Structure Model in a rfile format and the corresponding ifile format. The 'Ruptures' folder contains the projected rupture models for each earthquake on the Japan Trench mesh and the corresponding 100 realizations of the mean rupture models generated using FakeQuakes. If you use these data, please cite the associated publication:</p> <p>Fadugba, ­O. I., Sahakian, V. J., Diego Melgar, D., Rodgers, A. & Shimony, R. (2023). The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</p>
Eurasia-fixed GNSS-derived velocity field for Turkey
<p>The dataset and the analysis is described in the <a href="http://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=1844&context=earth">paper</a> and presented <a href="https://meetingorganizer.copernicus.org/EGU23/EGU23-12258.html">here</a>. The data fields are: longitude, latitude, E velocity, N velocity, sigma E, sigma N, rho, U velocity, sigma U, station. You can use the following code snippet to load your data to python:</p> <pre><code class="language-python">fields = ['longitude', 'latitude', 've', 'vn', 'se', 'sn', 'rho', 'vu', 'su', 'station'] df = pd.read_csv('Kurt_etal_2023.csv', header=None, names=fields)</code></pre> <p> </p>
Compact RINEX dataset of: Optimizing simultaneous water level and wave measurements from multi-GNSS interferometric reflectometry over one year at an exposed coastal site
<p>Compact-RINEX files (Hatanaka-compressed format) for each day of the deployment. Sampling frequency is 1 Hz.</p>
Average decomposed InSAR-GNSS surface velocities for Iran
<p>Average East and vertical InSAR-GNSS surface velocities for Iran at a 0.005° pixel spacing (approximately 500 m), derived from COMET-LiCS interferograms (https://comet.nerc.ac.uk/COMET-LiCS-portal/) through the use of LiCSBAS (https://github.com/yumorishita/LiCSBAS) and "decompose_insar_velocities" (https://github.com/andwatson/decompose_insar_velocities).</p> <p>File details:</p> <ul> <li>vE and vU refer to the East and vertical velocities, respectively.</li> <li>sE and sU refer to the East and vertical 1-standard deviation uncertainties, respectively.</li> <li>A positive velocity in vE signifies motion to the East, while a positive velocity in vU signifies upward motion.</li> <li>All four files are stored as geotiffs, created using the Matlab "geotiffwrite" function.</li> </ul>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Slip model of the 2022 Mw 6.6 Luding earthquake from inversion of GNSS and Sentinel-1A satellites
<p>2022年Mw 6.6泸定地震的滑移模型,来自GNSS和Sentinel-1A卫星的反演“2022年Mw 6.6泸定地震滑移模型,GNSS和Sentinel-1A卫星反演”中使用的GNSS数据</p>
Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.
<p>Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.</p><p> </p>
Near-field tsunami forecasting along the Cascadia subduction zone using rapid GNSS source models
<p>Data pertaining to the submitted work <em>Near-field tsunami forecasting along the Cascadia subduction zone using rapid GNSS source model</em> (2020) by Williamson et al. Specifically, the dataset includes (1) synthetic, generated GNSS waveforms for 1300 Cascadia rupture scenarios and (2) model outputs for every tested rapid earthquake characterization.</p>
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