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36 results for “ground penetrating radar”
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks
<p><strong>Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis.</strong></p> <p><strong>The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard.</strong></p> <p><strong>This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).</strong></p>
Ground Penetrating Radar survey of San Quintin glacier, Northern Patagonia Icefield
<p>Measured ice thickness, Residual Bedrock Reflection Power (BRP) and Internal Reflection Power (IRP) of San Quitin glacier, Northern Patagonia Icefield.</p> <p> </p> <p>Data for the preprint "Frontal collapse of San Quintín glacier (Northern Patagonia Icefield), the last piedmont glacier lobe in the Andes" in review for The Cryosphere (https://doi.org/10.5194/tc-2023-10)</p> <p> </p>
Ground Penetrating Radar data acquired over Von Postbreen, Svalbard, March 2018.
<p>GPR data and diffraction focusing Madagascar code</p> <p>Data authors: Richard Delf and Robert Bingham, University of Edinburgh.</p> <p>Data and code associated with Delf et al. "Reanalysis of polythermal glacier thermal structure using radar diffraction focussing"</p> <p>Ground-Penetrating Radar data was acquired over Von Postbreen, Svalbard, to investigate the internal velocity structure by diffraction focusing.</p> <p>Data were acquired in March 2018 over 2 days, using a PulseEkko Pro 25 MHz system, towed behind a snowscooter in an in-line, common-offset configuration. Data are stored in the SEGY format.</p> <p>Differential GPS was used for positioning; coordinates are included in the SEGY data.</p> <p><strong>Data Description</strong></p> <p>Three groups of data are present here within the .zip file.</p> <p>data with no preprocessing: files in ./data/aq_data/18_VP_0_*.SGY</p> <p> SEGY Radar data with pre-processing applied. Significant ringing is observed in the upper regions of the radargrams. Note file names do not correlate with the files in subsequent folders.</p> <p>data with preprocessing: filenames in ./data/raw_data/18_VP_1_*.SGY</p> <p> SEGY Radar data with pre-processing applied to remove ringing and other noise using an SVD filter and bandpass filtering. Radargrams are sorted into shorter lines across and up the glacier. See associated paper for additional details.</p> <p>processed_data: filenames 18_VP_2_*.SGY</p> <p> The same data as above, with diffraction coherence after Schwarz et al (2019) applied. See associated paper for additional details.</p> <p> </p> <p><strong>Processing files:</strong></p> <p>SConstruct: Madagascar SConstruct file for processing the above profiles to derive the velocity profiles described in the associated (Delf et al) paper.</p>
Ground penetrating radar (GPR) measurements in the Lower Muschelkalk of a limestone quarry in Rüdersdorf near Berlin, Germany 2023
<p>Surface Ground Penetrating Radar (GPR) was used on the exposed limestone of a quarry. Several measurements were carried out using a 200 MHz antenna, including a test field with densely spaced profiles suitable for 3D visualization. The measured features are oriented along boreholes and the actively mined demolition edge. Photographs of the wall face are provided at different stages of mining, which extended into the previously measured test field, revealing its cross section.</p>
Ground penetrating radar (GPR) monitoring of a densely gridded survey field in the Lower Muschelkalk of a limestone quarry in Rüdersdorf near Berlin, Germany 2023/24
<p>Surface Ground Penetrating Radar (GPR) was used on the exposed limestone of a quarry to monitor a survey field of densely spaced profiles on three dates (in October 2023, December 2023 and February 2024). The different moisture conditions of these survey dates can be evaluated with linked detailed weather data (<span>10.5281/zenodo.13867069</span>). A time-depth conversion using CMP data to calculate the EM wave velocity suggested a GPR penetration depth of approximately 4 metres. The measurements were planned, carried out and analysed in the context of a Master's thesis on the potential of GPR to investigate the hydrodynamics of carbonate rocks relevant to groundwater recharge processes.</p>
Ground-penetrating radar and shallow firn cores from Devon Ice Cap, Canadian Arctic
<p>GPR data and firn cores were collected over Devon Ice Cap, Canadian Arctic in May 2015.</p> <p>-----------------------------------------------------------------</p> <p><strong>Firn cores</strong></p> <p>Six ~11 m long firn cores were drilled using a Kovacs drill (9 cm diameter) along the GPR profiles. Pictures were taken of the firn cores, which were subsequently used to log the firn facies. From each core, three sections at different depths that did not include ice layers were weighted with a digital scale and used to calculate the firn density. At each firn core location, the snow depth was recorded, as well as at an additional location where a snow pit was dug (SPB1).</p> <p><em>DIC_firn_cores_2015_density.xlsx</em>: Firn core density measurements. Three measurements were taken from each core, using ice-free sections. </p> <p><em>DIC_firn_cores_2015_stratigraphy.xlsx</em>: Firn stratigraphy for each core location, derived from the firn core pictures. F stands for firn, I for ice layer, and P for percolation pipe/feature (ice in the firn core that does not present as an ice layer throughout the core diameter).</p> <p><em>DIC_snowdepth_2015.xlsx</em>: Snow depth measurements at each core location.</p> <p><em>Firn_core_pictures.zip</em>: Pictures of the firn cores taken with infrared and visible light cameras.</p> <p>-----------------------------------------------------------------</p> <p><strong>GPR data</strong></p> <p>GPR data were collected with a PulseEKKO Noggin radar (Sensors & Software Inc.) with 500 MHz center frequency antennae (i.e., 0.6 m wavelength). The antennae were mounted on a plastic sled towed by snowmobile, generating a data set sampled every ~0.4 m along track. Positioning was obtained with a Leica Geosystems GPS system providing a 25 cm RMS accuracy.</p> <p>Processing of the GPR data was performed in Matlab and included dewow filtering, time-zero shift, background removal, Butterworth band-pass filtering and the application of a gain function.</p> <p><em>PulseEkko_RawData</em>: Folder containing the raw PulseEKKO GPR and GPS files.</p> <p><em>PulseEkko_ProcessedData</em>: Contains the processed GPR data as .mat files. Description of the data files can be found in <em>ProcessedData_readme.txt</em>.</p> <p> </p>
Comparison of Dielectric Properties and Structure of Lunar Regolith at Chang'e-3 and Chang'e-4 Landing Sites Revealed by Ground Penetrating Radar
<p><strong>Fig 2(d) dataset.</strong> Signal Power profile and after R<sup>2</sup>, R<sup>3</sup>, R<sup>4 </sup>backscatter/spreading correction. The first column is depth in meter, second column is original data, third, forth, fifth column is original data after R<sup>2</sup>, R<sup>3</sup>, R<sup>4</sup> correction,respectively.</p> <p><strong>Fig 3(b) dataset. </strong>The first five days of Lunar penetrating radar (LPR) of CE-4 site with Auto Gain Control (AGC) method. Each column represents a single sample of data.</p> <p><strong>Fig 3(c) dataset. </strong>LPR dataset of CE-4 site using an exponential equation gain function for amplitude compensation. Each column represents a single sample of data.</p>
MERL Ground Penetrating Radar Dataset (MERL-GPR)
<p>MERL-GPR is a simulated ground penetrating radar dataset generated using the open-source finite difference time domain tool for electromagnetic simulation gprMax. The dataset consists of 400 two dimensional underground structures with a domain size of 0.5m x 0.5m. The structures are composed of three layers, where the top layer is air with depth of 0.15m, and the bottom two layers are ground material with a total depth of 0.35m. The source is located 0.1m above the ground and emits a standard Ricker wavelet source with center frequency of 1GHz. The depth of the second ground layer d2 is sampled from a uniform distribution U(0.1, 0.3) and the depth of the first ground layer is d1 = 0.35 – d2.</p> <p>The first ground layer has a permittivity sampled from U(3, 5) and the second ground layer has permittivity sampled from U(5,10). Two cylinders are embedded int eh second ground layer. One cylinder is composed of air (permittivity 1), whereas the permittivity of the second cylinder is sampled from a uniform distribution U(3,10). Both cylinders have radii sampled from U(0.03, 0.06).</p> <p>From the time domain data generated by gprMax, we apply the Fourier transform and extract the wavefields with frequencies within the [0.5GHz, 1.5GHz] band discretized over 50 frequencies.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~1.72GB</li> <li>The data directory contains both the freespace response as well as the total wavefield measured at every pixel in the computational domain.</li> <li>The complex frequency coefficients of the source wavelet are also provided.</li> <li>Two trained models are provided in the forward_model sub-directory, one model corresponds to the vanilla FNO architecture with 10 layers and the other model corresponds to the proposed BornFNO architecture also with 10 layers.</li> <li>A pretrained autoencoder is also provided under the priors sub-directory.</li> </ul> <p><strong>Other Resources</strong></p> <p>Pytorch code for training the models and solving the inverse problem is available at https://github.com/merlresearch/DeepBornFNO.</p> <p><strong>Citation</strong></p> <p>If you use MERL-GPR in your research, please cite our paper:</p> <pre><code>@InProceedings{ Zhao_2023ICASSP, author = {Qingqing Zhao and Yanting Ma and Petros Boufounos and Saleh Nabi and Hassan Mansour}, title = {Deep Born Operator Learning for Reflection Tomographic Imaging}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = 2023, month = June } </code></pre> <p><strong>Copyright and License</strong></p> <p>The MERL-GPR dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Raw Date of Manuscript 《Quantifying the ice storage in the Upper Indus River basin with the ground-penetrating radar measurements and Glacier Bed Topography version 2 modeling》
<p>Raw date and materials of the manuscript 《Quantifying the ice storage in the Upper Indus River basin with the ground-penetrating radar measurements and Glacier Bed Topography Version 2 modelling》</p>
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.
Estimating belowground carbon stocks in isolated wetlands of the Northern Everglades Watershed, central Florida, using ground penetrating radar (GPR) and aerial imagery
<p>This data set includes raw GPR profiles for isolated wetlands in the Disney Wilderness Preserve (Kissimmee, FL) for the purpose of below ground soil C stock estimations. </p>
Analysis of Orbital Sounding in Context with In Situ Ground Penetrating Radar at Jezero Crater, Mars
<p>This release includes all the SHARAD data used to produce analysis and figures in the paper, "Analysis of Orbital Sounding in Context with In Situ Ground Penetrating Radar at Jezero Crater, Mars", by M.C. Raguso et al., submitted to Geophysical Research Letters in February 2024.</p> <p>The manuscript is currently under review. The dataset will be released following the completion of the review process.</p> <p>This release also includes slides (pdf format) presented during the RIMFAX Science Team meeting (09/6/22-09/09/22).</p> <p>Preferred citation (DataCite format): </p> <p>Raguso, M.C., & Nunes, D.C. (2024). Analysis of Orbital Sounding in Context with In Situ Ground Penetrating Radar at Jezero Crater, Mars. [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10681430">https://doi.org/10.5281/zenodo.10681430</a></p> <p><em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file.</em></p>
Pea Island, NC, USA Ground Penetrating Radar and Grain Size Data
<p>Ground penetrating radar data are in a file system for the RADAN software (GSSI). The excel files are outputs from the Malvern Mastersizer 3000 laser particle size analyzer in bin sizes. </p>
Brief Communication: Monitoring active layer dynamic using a lightweight nimble Ground-Penetrating Radar system. A laboratory analog test case
<p>GPR dataset from Leger et al., 2023 (https://tc.copernicus.org/preprints/tc-2022-214/)</p> <p> </p>
Amery Ice Shelf Grounding Line Datapoints Extraction from Airborne Ice-penetrating Radar
<p>We present a new grounding line product for Amery Ice Shelf - the ice-penetrating radar-derived grounding line points. The 137 grounding line points were identified by 53 survey lines from 2017 to 2020 and classified into three categories. The 'Class 1' points are extracted by significant echo reflection changes between ice-bed and ice-seawater interfaces along survey lines with continuous signal and have the highest accuracy. The 'Class2' and 'Class 3' points were derived from survey lines with 'fuzzy region' with lower accuracy. The mean interval of radar-derived points is 16.4 km. The 'best case' radar-derived positions (Class 1) and those from satellite data reveals a mean separation of 1.00±1.16 km. Two products are available: (1) ice thickness data of Amery Ice Shelf from ice-penetrating radar lines; (2) radar-derived grounding line position (137 points). The ice thickness was calculated by ice surface and bottom signal and the unit is m. The radar-derived grounding line position have six fields, latitude, longitude, id, ice thickness, date (for collecting the radar data) and Class (the category of point).</p>
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Open the record for dataset details and reuse information.
Ground-Penetrating Radar (GPR) Data from 41PT283 in Potter County, Texas
<p>On June 16, 2016, a ground-penetrating radar (GPR) survey was conducted at an Antelope Creek site (41PT283) on the Bureau of Land Management's Cross Bar Management Area in Potter County, Texas. These data represent raw, unprocessed data collected along the X-axis of a 13m (X-axis) x14m (Y-axis) grid at 50cm intervals. This survey is linked to an existing grid from previous investigations at the site in 2007 and 2008. These data will serve as a comparative reference for additional GPR surveys in the area of the Canadian River basin, can be used as an educational dataset to teach GPR processing techniques, and is representative of one of the first GPR surveys at an archaeological site in the Texas panhandle. This dataset includes 29 transects and an illustrated grid.</p> <p>Many thanks to Ryan Howell, Adrian Escobar, and the Bureau of Land Management (BLM) for requisite permissions and access.</p>
Ground Penetrating Radar (GPR) survey of four artificial ice reservoirs (icestupas)
<p><strong>Objective</strong></p> <p>The objective of this project is to establish that non–invasive subsurface imaging with ground penetrating radar (GPR) is a viable method to map internal structure of artificial ice reservoirs(AIR) and to estimate volume. Particular objectives were the following:</p> <ol> <li><strong>Volume validation : </strong>GPR surveys could be used to validate volumes of AIRs.</li> <li><strong>Spatial density resolution : </strong>GPR data could help identify air, snow and ice layers within the AIR.</li> </ol> <p><strong>GPR Field Work</strong></p> <p>GPR survey has been conducted on four Ice Stupas at locations namely Phaterak, Gangles, Kullum and Takmachik in March 2020 as part of an <a href="https://swisspolar.ch/2021/05/artificial-ice-reservoirs-of-ladakh-suryanarayanan-balasubramanian/">Swiss Polar Institute funded expedition</a>.</p> <p><strong>Why Ground Penetrating Radar?</strong></p> <p>Ice is very transparent to GPR signals, allowing tremendous penetration. GPR is also sensitive to subtle changes in the properties of ice layers. This makes it a powerful tool to image the internal structure of glaciers and ice sheets at a scale of meters or hundreds of meters. The basic principle of a pulsed GPR system is to send an electromagnetic signal into the ground and to record the signal reflections as a function of their two-way travel time. Partial reflections of the electromagnetic wave recorded as internal reflection horizons (IRH) occur at vertical discontinuities in the dielectric material. From polar studies, IRH are known to coincide with variations in density, acidity (Robin et al., 1969), liquid water content (Forster et al., 2014) and changes in crystal orientation fabric.</p> <p><strong>Archive contents</strong></p> <p>The archives contents are organized in four separate directories. Each directory contains GPR data of one icestupa. Apart from this, a preliminary report is also attached produced by the GPR company (SHIJAY PROJECTS).</p>
Pre-ABoVE: Ground-penetrating Radar Measurements of ALT on the Alaska North Slope
This data set includes estimates of permafrost Active Layer Thickness (ALT; cm), and calculated uncertainties, derived using a ground-penetrating radar (GPR) system in the field in August 2014 near Toolik Lake and Happy Valley on the North Slope of Alaska. GPR measurements were taken along 10 transects of varying length (approx. 1 to 7 km). Traditional ALT estimates from mechanical probing every 100 to 500 m along each transect are also included. These data are suitable for future studies of how ALT varies over relatively large geological features, such as hills and valleys, wetland areas, and drained lake basins.
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