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245 results for “geophysical”
Supplementary materials for: Multiple ruptures on the North Sofia fault at Gorni Bogrov from scarp profile and shallow geophysics
<p>The data set contains resistivity data of two profiles that were measured at Gorno Bogrov site in the Sofia basin in Bulgaria. The resistivity survey was performed to study the North Sofia fault.</p> <p>The resistivity data in files r1ohm.tx and r2ohm.txt should be inverted using the BERT software (http://resistivity.net/) with configuration files r1cfg.txt and r2icfg.txt, respectively. The file bog2.txt contains fault coordinates for constraining the inversion mesh of the shorter profile (files r2ohm.txt, r2icfg.txt).</p> <p>The data set was collected within the project Assessment of Earthquake Ground Motion Amplification in the Sofia Basin (<a href="http://sofiabasin.atwebpages.com/">http://sofiabasin.atwebpages.com/</a>), funded by the Bulgarian National Science Fund, contract number KP-06-N64/1 from 15.12.2022.</p>
Digitized geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy) collected from public sources.
<p>The database is a supplementary material of:</p><p>Livani, M., Petracchini, L., Benetatos, C., Marzano, F., Billi, A., Carminati, E., Doglioni, C., Petricca, P., Maffucci, R., Codegone, G., Rocca, V., Antoncecchi, I. (2023). Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). Earth System Science Data Discussions, 15, n. 9, 4261-4293, doi: 10.5194/essd-15-4261-2023</p><p> </p><p>This database comprises subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). We realized the database by collecting, revising, and digitizing data, originally in raster format, from public sources. These data have been then used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units.</p><p>The data have a common geographical system: WGS 84/UTM zone 32N; EPSG: 32632.</p><p>The database contains borehole information from 160 deep wells (i.e., wellhead coordinates, rotary table elevation, measured depth, true depth, total depth and deviation survey) and digitized Spontaneous Potential, Gamma Ray, and Sonic logs. Five horizons were digitized from 61 geological cross-sections and from 10 isobath maps that roughly correspond to the boundaries of units showing different lithological properties and with different mechanical properties. The horizons are, from the oldest to the youngest: the top of the magnetic basement, the top of the carbonate succession, the base of the Pliocene, the base of the Calabrian and the base of recent continental deposits. In addition, the gridded surfaces of the 3D geological model are available.</p><p>We organized the database into two groups: "primitive data" and "derived data".</p><p>Primitive data are the result of the digitization of public data. The database of the primitive data is formed by the main horizons reported in the geological cross-sections, the isobaths of the main geological surfaces, the well locations, comprised their trajectory along depth, lithological and stratigraphical information, and geophysical logs from composite well logs. Well data include specific sets of well logs aimed to geological/mechanical characterization of the geological units (e.g., Spontaneous Potential log, Resistivity log, Gamma Ray log, and Sonic log).</p><p>Derived data consist of two datasets: i) the primitive isobaths maps and geological cross-sections data that have been filtered and verified after a data accuracy analysis performed to unravel discrepancies in the interpretation of the subsurface geological horizons; ii) a set of regional surfaces of the main geological units of the Po Plain subsurface. These surfaces were generated by interpolating the filtered primitive data and without considering the fault occurrence/displacements.</p><p>Primitive and derived data are provided in delimited text file format organized according to the data type (i.e., well, geological cross-section, map and gridded surface). The format and the organization of data are explained in the related "readme" file presents within each data folder.</p><p>Our database represents a collection of the main published works regarding the Po Plain. Detailed studies related to specific sectors of the Po Plain might not be present in our database.</p><p>Further details about the data processing and organization are given in the related manuscript.</p><p> </p>
Supplementary Data to: "Quantifying sub-seasonal growth rate changes in fossil giant clams using wavelet transformation of daily Mg/Ca cycles" in Geochemistry, Geophysics, Geosystems
<p>El/Ca data and high resolution images of 3 laser-ablation tracks on a fossil giant calm. The following Isotopes were monitored <sup>11</sup>B, <sup>23</sup>Na, <sup>24</sup>Mg, <sup>27</sup>Al, <sup>43</sup>Ca, <sup>88</sup>Sr, <sup>89</sup>Y and <sup>138</sup>Ba. The data was measured with laser-ablation inductively coupled plasma mass spectrometry (LA-ICPMS) using a 3 x 33 µm laser slit. El/Ca ratios were calibrated using NIST SRM 612 as bracketing external standard (Jochum et al., 2011) with updated Mg values from Evans & Müller (2018) and <sup>43</sup>Ca as the internal standard; data quantification follows Longerich et al. (1996) and was performed using the software iolite 4 (Paton et al., 2011). For details see main text.</p> <p>References:</p> <p>Evans, D., & Müller, W. (2018). Automated Extraction of a Five-Year LA-ICP-MS Trace Element Data Set of Ten Common Glass and Carbonate Reference Materials: Long-Term Data Quality, Optimisation and Laser Cell Homogeneity. <em>Geostandards and Geoanalytical Research</em>, <em>42</em>(2), 159–188. https://doi.org/10.1111/ggr.12204</p> <p>Jochum, K. P., Weis, U., Stoll, B., Kuzmin, D., Yang, Q., Raczek, I., Jacob, D. E., Stracke, A., Birbaum, K., Frick, D. A., Günther, D., & Enzweiler, J. (2011). Determination of Reference Values for NIST SRM 610–617 Glasses Following ISO Guidelines. <em>Geostandards and Geoanalytical Research</em>, <em>35</em>(4), 397–429. https://doi.org/10.1111/j.1751-908X.2011.00120.x</p> <p>Longerich, H. P., Jackson, S. E., & Günther, D. (1996). Inter-laboratory note. Laser ablation inductively coupled plasma mass spectrometric transient signal data acquisition and analyte concentration calculation. <em>Journal of Analytical Atomic Spectrometry</em>, <em>11</em>(9), 899–904. https://doi.org/10.1039/JA9961100899</p> <p>Paton, C., Hellstrom, J., Paul, B., Woodhead, J., & Hergt, J. (2011). Iolite: Freeware for the visualisation and processing of mass spectrometric data. <em>Journal of Analytical Atomic Spectrometry</em>, <em>26</em>(12), 2508–2518. https://doi.org/10.1039/C1JA10172B</p>
Synthetic data for the solution of geophysical inverse problems
<p>The synthetic LAB used to define an inverse problem is discretized in different number of parameters. Each "LAB" file is one specific discretization and contains the coordinatates X and Y and the value of the LAB at each position</p> <p>The reference velocity fields obtained from solving the Stokes equation with the synthetic LABs are found in "velo" files. They contain, X, Y, and Z and the field Vx, Vy and Vz.</p> <p>Another synthetic data set represents the african lithosphere. The first two columns of its LAB file contain the longitude and latitude of each point and the third column is the value of the LAB. The reference velocity field obtained from solving Stokes is found in the corresponding "velo" file.</p>
Characterizing the groundwater flow pathways and recharge sources of a desert inter-dune aquifer system by geophysical approaches and multiple isotopes (B, H and O)
<p>Major cations and anions, and H, O, and B isotope data of South Sumujaran Lake of Badain Jaran Desert, China</p>
Ivrea Geophysical Body (IGB) model geometry
<p>Ivrea Geophysical Body (IGB) model geometry</p> <p>Original publication: Scarponi et al. (2020), Geophys J Int, doi: 10.1093/gji/ggaa263</p> <p>We here provide our reference density model for the Ivrea Geophysical Body (IGB), developed by means of 3D gravity data modelling. The IGB model is defined by a single crustal interface, extending across the study area. <br> The density contrast associated with the interface is +400 kg/m3.</p> <p>Study area (latitude, longitude in degrees):</p> <p>- min longitude: 7.5 E<br> - max longitude: 9.0 E<br> - min latitude: 45.4 N<br> - max latitude: 46.3 N</p> <p>Study area lower left corner in latitude, longitude: <br> - 45.4 N, 7.5 E <br> This point is marked as a red circle in the presented figures, and it is used as the origin for the Cartesian reference frame in kilometres (x[km],y[km]). </p> <p>Files' content:</p> <p>- 3D_IGB_Model_Geometry.txt: <br> <br> ASCII text file containing the model geometry in the format:<br> <br> x[km], y[km], lon[deg], lat[deg], depth[km]<br> <br> The Cartesian reference frame (x[km],y[km]) has its origin in the lower left corner of the study area and extends up to 800 km distance from the origin (the study area is extended on purpose during gravity modelling, to avoid edge effects). Here we report the complete model geometry as extracted from the modelling software (IGMAS+).<br> * The reader should focus only on the study area domain, where gravity data has been measured and modelled! *</p> <p>- 3D_IGB_Reference_Model.fig: <br> <br> MATLAB .fig format file showing a rotatable 3D view of the interface defining the IGB model geometry.</p> <p>- 2D_IGB_CrossSections.png: <br> <br> .png format picture showing East-West oriented vertical cross sections of the IGB model. The view is towards the South across the cross-sections.</p> <p>For any question and/or discussion please contact the authors via email at: matteo.scarponi@unil.ch</p> <p>Best regards,<br> Matteo Scarponi and co-authors</p>
Dataset in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d</p>
Data Tables from contribution titled "Joint geophysical-petrological modeling on the Ivrea geophysical body beneath Valsesia, Italy: Constraints on the continental lower crust" published in Geochemistry, Geophysics, Geosystems
<p><strong>TABLE CAPTIONS</strong></p> <p><strong>Table 1: </strong>Average and specific bulk compositions of the representative exposed primitive rocks of the deep lower crust and upper mantle of the IVZ (Balmuccia and Lower Mafic Complex in Valsesia, Finero in Val Cannobina, and Sessera river in Val Sessera) used for the Perple_X calculations using the two thermodynamic models STX11 and HP02. Mn, Ni, P, and Cr were not used for the Perple_X calculations. Mg# is calculated as Mg / (Mg + Fe) (mole fractions). Symbols in the last row indicate the rock compositions that are reported (✓) and not reported (✗) in Figure 5. Average compositions are normalized to 100 wt.% on a volatile-free basis; specific compositions report original totals and H<sub>2</sub>O contents estimated based on the volumetric proportions hydrous minerals (amphibole and phlogopite) at the scale of the bulk rock. References: 1) Voshage et al. (1990); 2) Voshage et al. (1990), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 3) Pin and Sills (1986); 4) Pin and Sills (1986), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 5) Shervais and Mukasa (1991) and Mazzucchelli et al. (2009); 6) Sinigoi et al. (1991; 2011); 7) Hartmann and Wedepohl (1993); 8) Coltorti and Siena (1984); 9) Siena and Coltorti (1989), Lu et al. (1997a; b), and Stähle et al. (2001).</p> <p> </p> <p><strong>Table 2: </strong>Summary of <em>V<sub>P</sub></em> and <em>r</em> from the thermodynamic calculations based on representative rock compositions listed in Table 1. Mineral abbreviations: Pl = plagioclase, Spl = spinel, Grt = garnet, Ky = kyanite, Px = pyroxene, Cr-Di = Cr-diopisde, Am = amphibole, Phl = phlogopite.</p>
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
<p>3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)</p> <p>M. Jessell<sup>1,2</sup>, J. Giraud<sup>1,2</sup>, M. Lindsay<sup>1,2 </sup></p> <p><sup>1</sup>Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia</p> <p><sup>2</sup>Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia</p> <p>Contact author: Jeremie Giraud (jeremie.giraud@uwa.edu.au)</p> <p>Companion dataset to the paper:</p> <p>Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.</p> <p>This dataset contains models and data shown in the paper, in both 2D and 3D:</p> <p>1. Geological model</p> <ul> <li>Reference lithology voxet:</li> </ul> <p>The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.</p> <ul> <li>Probability voxet<br> The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.</li> </ul> <p>2. True and inverted models for density and magnetic susceptibility</p> <p>Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.</p> <p>3. Bouguer and total magnetic field anomaly</p> <p>Calculation is detailed in the paper.</p> <p>The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.</p> <p>It is a companion dataset to: <br> Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4452620">http://doi.org/10.5281/zenodo.4452620</a></p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487
<p>This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).</p>
Tarrant Keyneston Long Barrow - Geophysics
Gradiometer results for Tarrant Keyneston long barrow overlaid on Lidar Source: Objaverse 1.0 / Sketchfab
CESM2 Mechanically Decoupled (MD) for "Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming" - submitted to Geophysical Research Letters
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MD in paper)</li> <li>FC = fully coupled model (referred to as FC in paper)</li> </ul> <p>Decoding file names:</p> <ul> <li>ensmean refers to ensemble mean</li> <li>trend refers to linear trend over 1950-2014</li> </ul> <p>Variables:</p> <ul> <li>SST = sea surface temperature</li> <li>HMXL = mixed layer depth</li> <li>WSPDSRFAV = horizontal total wind speed average at the surface</li> </ul>
Geophysical data
<p>This publication contains lithological units (bodies numbered 31, 35, 61, 62, 63, 64, 90, 99), LitMod files (.out, .inp) as well as used by tAo to compute plate deflection (.PRM). We also added gridded data (netcdf grid format) and Bouguer and topographic profiles (.dat) used in Pajang et al. as well as Github link preseting our to install and run LitMod (ajay-Litmod_github.docx). </p> <p>gridded data are : </p> <p>1) crustal model - EPcrust.grd</p> <p>2) bouguer gravity data - grd_Aq_points.grd</p> <p>2) Vp and Vs data - pm_16km.grd and sm_16.grd</p> <p>3) LAB models - SL2013sv.grd and LITHO1_EU.grd</p>
Supplementary tables for Geophysical Research Letters_2021GL096049
<p>Data associated with the publication "Significant increase of continental freeboard during the early Paleoproterozoic: Insights from metasediment-derived granites"</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Dataset of "Geophysical Investigation of a Granite Exfoliation Dome: Enchanted Rock, Texas"
<p>This repository contains the dataset of a paper titled: "Geophysical Investigation of a Granite Exfoliation Dome: Enchanted Rock, Texas".</p> <p>This data was made possible in collaboration with the University of Houston - Department of Earth and Atmospheric Sciences and the summer Geophysical Field Camps of 2018 and 2019. Special thanks to the staff of the Enchanted Rock State Natural Area for granting survey access to the site.</p>
HPC geophysical electromagnetics: a synthetic VTI model with complex bathymetry
<p>Castillo-Reyes, O., de la Puente, J., Cela, E. J.M. (2022) HPC geophysical electromagnetics: a synthetic VTI model with complex bathymetry. Submitted to Energies Journal</p>
Data for Jucker and Goyal, Ozone-forced Southern Annular Mode during Antarctic Stratospheric Warming Events, Geophysical Research Letters 2022
<p>Data from climate model simulations and reanalysis which were analysed in associated paper.</p> <p>Read README.md file for detailed information on each file.</p>
Towards accurate quantification of ice content in permafrost of the Central Andes, part I: geophysics-based estimates from three different regions
<p>This data set contains two zip-files for a) the electrical resistivity tomography (ERT) data, and b) the refraction seismic tomography (RST) data used in</p> <p>Hilbich, C., Hauck, C., Mollaret, C., Wainstein, P., and Arenson, L. U.: Towards accurate quantification of ice content in permafrost of the Central Andes, part I: geophysics-based estimates from three different regions. The Cryosphere, 16, 1–28, https://doi.org/10.5194/tc-16-1845-2022.</p> <p> </p> <p><strong>1) Files included in allERT_Zenodo.zip:</strong></p> <p>(i) all ERT raw data files used in the paper,</p> <p>(ii) Topo_allERT.xlsx --> with the topography data of all profiles</p> <p>(iii) readme_ERT_files.txt --> with further explanations</p> <p> </p> <p><strong>2) Files included in allRST_Zenodo.zip:</strong></p> <p>(i) all RST raw data files used in the paper,</p> <p>(ii) Topo_xxx.txt with the topography data of each profile</p> <p>(iii) readme_RST_files.txt --> with further explanations</p> <p> </p> <p>Please consult the readme files first.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.