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9 results for “Electrical Resistivity Tomography”
Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain
<p><strong>Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup> and James Irving<sup>3</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p><sup>3</sup> Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>Electrical Resistivity Tomography (ERT) profiles were collected around the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). All data were collected with a <a href="http://www.iris-instruments.com/syscal-pro.html">Syscal Pro</a> Switch 48 from Iris Instruments, using an array of maximum 48 electrodes with a spacing between 1 and 10 meters. For each line, measurements were performed using a Dipole-Dipole (dd) and a Wenner-Schlumberger (ws) electrode configuration.</p> <p>All ERT lines locations can be visualized in <em><strong>ERT_map_lines_2019-2021.jpg</strong>.</em></p> <p>A result overview can be vizualized in <em><strong>ERT_allResults_3Doverview.png</strong>.</em></p> <p><strong>Data Structure</strong></p> <p>Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses.</p> <ul> <li><em><strong>1_createInput_prosys_to_pygimli.ipynb</strong></em> : Transforms the raw data from Syscal Pro (exported with <a href="http://www.iris-instruments.com/download.html">ProsysII</a> software as .csv) to a processed .dat file formated for inversion using the <a href="https://www.pygimli.org/">pyGIMLi</a> library.</li> <li><em><strong>2_ERT_inversion.ipynb</strong></em> : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> <p>In <strong>ERT_data.zip</strong>, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains :</p> <ol> <li><strong>GPS </strong>: folder with electrodes coordinates for each ERT line</li> <li><strong>inputGiMLi</strong> <ul> <li><strong>prosys_csv</strong>: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li><strong>input_ERT </strong>: stores the processed .dat file. (created from notebook 1)</li> <li><strong>results_lambda</strong> : contains a .png image with the inversion results using different values of the regularization parameter lambda used to assess the sensitivity of the inversion results (over/underfitting). Analysis is performed for each line and each electrode configuration (dd or ws). (created from notebook 2)</li> <li><strong>results_final</strong> : contains a .png image with the final inversion results for each line and electrode configuration (dd or ws) using the optimal lambda parameter only (all arrays are shown from East to West). (created from notebook 2)</li> <li><strong>vtk</strong> : contains a .vtk file for each final results for 3D vizualization in the <a href="https://www.paraview.org/">Paraview</a> software.</li> </ul> </li> <li><strong><em>ERT_line_description_yyyy.csv</em> </strong>: a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> <p>The <strong>results </strong>folder contains :</p> <ul> <li><strong>ERT_3Dview_paraview</strong> folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li><em><strong>ERT_results_all.pdf</strong></em> : A summary of all final results for all years (similar content as <em>ERT/inputGiMLi</em><strong>/</strong><em>results_final</em> folders)</li> <li><em><strong>ERT_results_bedrock.pdf</strong></em> : Contains the vizualization of specific ERT profiles in the outwash plain and their field location . The separation between a surface layer of water-saturated sediments (resistivity <2500 Ωm) and the underlying bedrock is delimited. The likely presence of buried ice blocks (isolated blocks with resistivity >5000-10000 Ωm) is also highlighted.</li> <li><em><strong>ERT_timelapse_salt_tracer.gif</strong></em> : results of a time-lapse ERT measurement performed on 9 August 2019 to track the movement of a salt plume injected at 06 am, 9.38 meters upslope (see paper by<em> Müller et al., 2022</em> for detailed analysis). The tracer starts to appear at 12:45 at a distance of 30m on the array. Minimum resistivity is reached at between 16:45 and 17:45.</li> </ul>
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>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Electrical tomography resistivity (ERT) monitoring time series
<p>Multi-temporal electrical tomography resistivity (ERT) measurements for monitoring the performance of the bio-degradable bentonite mat in OAL-Austria. The first measurement was conducted on 30 July 2020 before the implementation of the mat, afterwards seasonal measurement (except for winter due to snow cover) were obtained: 19 Oct 2020, 27 Arpil 2021, 10 August 2021, 4 October 2021, 13 April 2022. A time-lapse inversion algorithm was used to prepare the final results. See OPERANDUM deliverable 4.6 for more details.</p> <p>Device: Lippmann 4point light 10 W</p>
Datasets for Electrical Resistivity Tomography HotBENT-Lab experiment
<p>The dataset comprises Electrical Resistivity Tomography (ERT) data from the HotBENT-Lab experiments, which are aimed at understanding moisture dynamics and related swelling effects in bentonite under thermal-hydrological-mechanical-chemical (THMC) conditions. These experiments simulate environmental conditions similar to those in geological repositories for high-level radioactive waste.</p> <ol> <li> <p><strong>calibration.csv</strong>: This file contains ERT data collected from HotBENT-Lab 2, which was designed to closely mimic field conditions by using a higher dry density (1.45 g/cm³) and reduced initial water content (5.3%). The dataset includes detailed electrical conductivity measurements over time and across the experimental columns, which were used to estimate water content and track changes due to swelling effects.</p> </li> <li> <p><strong>c1c2_midtc_data.csv</strong>: This file contains ERT data from HotBENT-Lab 1, which serves as a foundational experiment with a lower dry density (1.2 g/cm³) and higher initial water content (18%). This dataset captures early-stage moisture distribution and temperature effects, allowing for comparative analysis of swelling and conductivity changes.</p> </li> </ol> <p>Both datasets are integral for validating the multi-scale ERT-based framework used to estimate moisture dynamics and assess the scalability of petrophysical models in controlled laboratory conditions.</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Electrical Resistivity Tomography measurements of a limestone wall during fires
<p>The dataset comprises median reisistivity from resistivities acquiered during fires taking place in an underground limestone quarry. It comprises 24 lines, corresponding to each of the time an ERT image was acquired (the time = 0 is the time the fire was ignited); and 9 columns corresponding to each depth (median calculated on a 2-cm thick interval).</p>
Dataset for "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography"
<p>This dataset includes soil temperature, moisture, and electrical conductivity measurements taken at a depth of approximately 50 cm, as well as the digital elevation model, electrical resistivity tomography, and self-potential data used in the manuscript "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography" submitted to <em>Comptes Rendus Geoscience</em>.</p>
Deep Electrical Resistivity Tomography - SENECA Project Antarctica
<p>This repository collects all the files of the <strong>Deep Electrical Resistivity Tomography (DERT) geophysical survey</strong> held in Antarctica (Dry Valleys) on December 2019- January 2020, as part of the SENECA Project (PrincipaI Investigator Livio Ruggiero, INGV, now at ISPRA).</p> <p>Files in this repository are organized as follows:</p> <ul> <li>The <strong>dem</strong> folder contains the Lidar xyz digital elevation model of the surveyed area (Fountain et al., 2017) that was used to appropriately model the terrain during the mesh generation step of the data processing.</li> <li>The <strong>field_data</strong> folder contains the DERT resistivity/chargeability datasets acquired by using the IRIS Instruments Fullwaver system (12 VFullwaver boxes, 50 meters MN dipoles) and a VIP5000 transmitter used to inject current at TX electrodes. More details on file types can be found in the folder's <em>README</em> file.</li> <li>The <strong>processing</strong> folder contains the resistivity inversions files for each profile. More details on file types can be found in the folder's <em>README</em> file.</li> <li>The <strong>topography</strong> folder hosts the csv files with the sensor positions for each profile from GPS survey and the processed files with the definitive coordinates of the RX and TX electrodes in the UTM WGS84 coordinate system. The conversion table txt files are also derived: these are used to translate the "dummy" coordinates of the raw datasets to the real positions.</li> </ul> <p><strong>Acknowledgments</strong></p> <p>This work is part of the PNRA 2018/D3.01 SENECA project and was financially supported by CNR (PNRA 2018 n° 00253 linea D, prot.73633/2019, SENECA PROJECT: Source and origin of greenhouses gases in Antarctica). All the data were collected during the XXXV Italian expedition in Antarctica, we thank PNRA and UTA ENEA for the logistic support. SENECA is a joint project of<br>international cooperation between Italy, New Zealand and Norway. We thank the Antarctica New Zealand for the scientific, logistical and technical support and the Scott Base personnel for hosting our research team. We acknowledge the support of the Research Council of Norway (NFR) through the HOTMUD project number 288299 and its Centres of Excellence funding scheme, project<br>number 223272 (CEED). </p> <p><strong>Credits</strong></p> <ul> <li>Jacob Anderson: data acquisition, data interpretation</li> <li>Massimiliano Ascani: data acquisition</li> <li>Bob Dagg: data acquisition</li> <li>Federico Fischanger: survey design, data processing and interpretation</li> <li>Richard Hardie: data acquisition</li> <li>Matteo Lupi: conceptual developement, data interpretation</li> <li>Adriano Mazzini: data acquisition</li> <li>Claudio Mazzoli: data acquisition</li> <li>Valentina Romano: survey design, data collection, data processing and interpretation</li> <li>Livio Ruggiero: principal Investigator of the SENECA project.</li> <li>Alessandra Sciarra: data collection</li> <li>Maria Chiara Tartarello: data collection </li> <li>Gary Wilson: data collection, data interpretation</li> <li>Rachel Worthington: data collection </li> </ul> <p> </p> <p><strong>References:</strong></p> <p>Fountain, A.G., Fernandez-Diaz, J.C., Obryk, M., Levy, J., Gooseff, M., Van Horn, D.J., Morin, P., & Shrestha, R., (2017), High resolution elevation mapping of the McMurdo Dry Valleys, Antarctica, and surrounding regions, Earth Syst. Sci. Data, 9, 435-443.</p>
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