Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “Full Waveform Inversion”
Final models for "Global-Scale Full-Waveform Ambient Noise Inversion" by Sager et al. (2020)
<p>The exodus model contains the inverted structure model and the source distribution can be found in the HDF5 file. Both can be visualized in ParaView. For the source model, we recommend opening it with the correspoding XDMF file (select "XDMF Reader" in the dialogue box).</p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event
<p>We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions. </p> <p>The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p>
A model of P-wave velocity beneath the greater Alpine region from teleseismic full P-waveform inversion
<p>The dataset provides values of P-wave velocity in a 3D spherical chunk beneath the greater Alpine region as they resulted from a teleseismic full waveform inversion of AlpArray data. </p> <p>Please find a description of the dataset in the accompanying README file.</p>
Dataset related to 'Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies'
<p>This repository contains the global distribution of sources and receivers, tomographic models (netCDF4 format), stacked waveforms from the wavefield modelling (.h5 format), 2D grids of the time-depth correlations (.csv format), and Python scripts required for the full analysis and figures presented in the manuscript.</p>
Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net
<p>Data presented in the manuscript titled 'Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net'</p>
Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324
<p><strong>Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324</strong></p> <p> </p> <p>Arthur Rodgers</p> <p><em>Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore CA 94551, USA</em>; and</p> <p><em>Department of Earth Sciences, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland</em></p> <p> </p> <p>rodgers7@llnl.gov</p> <p> </p> <p>10.5281/zenodo.11619519</p> <p> </p> <p><strong>Summary</strong></p> <p>This data set includes the metadata and model for the three-dimensional (3D) seismic Earth model WUS324 (Rodgers et al., 2024). This model describes seismic wavespeeds, density and attenuation for the 3D volume spanning the surface to 400 km depth, latitudes from Mexico to Canada (28 to 52) and longitudes from the Pacific Ocean to the Great Plains (-132 to -100). The metadata tabulates the earthquakes and seismic networks and stations used in the creation and validation of 3D seismic Earth model WUS324.</p> <p> </p> <p>The WUS324 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). https://doi.org/10.1016/b978-012387582-2/50038-1</p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148</p> <p>Rodgers, A., C. Doody and A. Fichtner (2024). WUS324: Converged Full Waveform Inversion Improves Waveform Fits While Imaging Crustal and Upper Mantle Structure in the Western United States, manuscript in preparation.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was initiated under Laboratory Directed Research and Development project 20-ERD-008 at Lawrence Livermore National Laboratory (LLNL) and continued with support from the National Nuclear Security Administration Ground-based Nuclear Detonation Detection program. AR is grateful to the Eidgenössische Technische Hochschule, Zürich for support as an Academic Guest and to LLNL for Professional Research and Teaching Leave. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. LLNL-MI-865269.</p> <p> </p> <p> </p> <p>Files contained in this data set.</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>WUS324_all_events_project.csv</p> </td> <td> <p>Table of all 216 events considered during the creation of WUS324. This table includes the origin date and time, location and moment tensor parameters.</p> </td> </tr> <tr> <td> <p>WUS324_all_networks.txt</p> </td> <td> <p>Table of all seismic networks and that contributed to WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_events.txt</p> </td> <td> <p>Table of the 126 event names used in the inversions that created WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the creation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_events.txt</p> </td> <td> <p>Table of the 65 event names used in the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.h5</p> </td> <td> <p>WUS324 model for simulating waveforms with minimum period of 16 seconds in Salvus HDF5 format.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.xdmf</p> </td> <td> <pre>Auxiliary file for WUS324_16sec.h5, used to import model into Paraview.</pre> </td> </tr> <tr> <td> <p>WUS324.nc</p> </td> <td> <pre>WUS324 model in netCDF format following the metadata standards of the Incorporated Research Institutions for Seismology Earth Model Collaboratory</pre> </td> </tr> </tbody> </table>
Constructing a 3-D radially anisotropic crustal velocity model for Oklahoma by using full waveform inversion
<p>The OK3D_Vp_Ani.csv and OK3D_Vs_Ani.csv is inverted 3-D compressive and shear velocity model proposed in the publication.</p> <p>Each file contains horizontally and vertically polarized velocity components and their relative perturbation with respect to the averaged 1-D velocity profile, as well as the RA defined in the paper, at each location (longitude, latitude, depth).</p> <p>These two files are stored in CSV format, and can be easily readed by pandas module in python environment.</p>
Imaging the northeast lobe of the Sudbury Structure through 2D and 2.5D visco-acoustic full-waveform inversion
<p>Seismic reflection profile LN182 - Northeast lobe of the Sudbury Structure. The LN182 transect comprises over 1,300 receivers (single wireless vertical-component 5-Hz geophones) and over 1,500 vibroseis sources. A linear upsweep of 5-120 Hz was generated by four vibroseis trucks forming the vibroseis source system. </p>
Current Full-Waveform Inversion of the Return Stroke Channel based on Single-Station Electric Field Observations
<p>In manuscript entitled "Current Full-Waveform inversion of the Return Stroke Channel Based on Single-Station Electric Field Oberbations", the data of rocket-triggered flash o901 was obtained during the SHATLE was used. The data of our results are including in the Data- for- figrue-x.fig. These files can be opened later. The data supports the aforementioned manuscript and can bue used freely for scientific purposed with appropriate citation.</p>
waveform data of the Empirical Green's Functions (EGFs) and earthquakes for the manuscript "Unraveling the Mantle Dynamics in Central Asia with Full Waveform Inversion Tomography"
<p>The dataset uploaded contains the original ASDF files and the SAC files (with '_sac') converted from ASDF</p> <p>For the ASDF files:</p> <p>The ASDF data file could be read through python module pyasdf and obspy after decompressing. </p> <p>The earthquake information could be accessed by the following command</p> <p>import pyasdf</p> <p>ds=pyasdf.ASDFDataSet(ASDFfile, mode='r')</p> <p>event=ds.events[0].preferred_origin()</p> <p>print(event)</p> <p>the waveforms coul dbe accessed through</p> <p>stream=ds.waveforms[net.station].raw_recording</p>
Convolutional-neural-network-based reflection full-waveform inversion
<p>The data is used by the paper "Convolutional-neural-network-based reflection 1 full-waveform inversion"</p>
Multi-scale full waveform inversion based on a convolutional neural network
<p>The research data from this paper are uploaded here and are available for download.</p>
Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies
Open the record for dataset details and reuse information.
Near-Surface Full-Waveform Inversion Reveals Bedrock Control on Critical Zone Architecture Resources
<p>Near-Surface Full-Waveform Inversion Reveals Bedrock Control on Critical Zone Architecture Resources</p> <p> </p>
Constructing a 3-D radially anisotropic crustal velocity model for Oklahoma using full waveform inversion
<p>The CSV file is the inverted 3D shear velocity. It contains absolute horizontally and vertically polarized shear velocity, as well as their relative perturbation with respect to the averaged 1-D velocity profile. The radial anisotropy (RA), which is defined in the paper, is also concluded in the file.</p>
A practical efficient elastic full-waveform inversion for multicomponent OBS data
<p>A practical efficient elastic full-waveform inversion is essential for multicomponent OBS data. It is used to obtain multiparameter estimation for the study of the seismology which can provide the much-needed insight into the earth's interior. In this letter, we present the relevant dataset to the paper. The dataset includes synthetic data which was simulated on the modified Overthrust model and the field data which was acquired from East China Sea. The synthetic data comprises 100 OBSs due to 400 sources. We present the results of our method on the synthetic data and field data and then compared it with traditional adjoint-state method and improved scattering method. </p> <p><strong>Dataset Contents</strong></p> <ul> <li>Synthetic data (common receiver gathers): <ul> <li>the number of sources: 400</li> <li>the number of OBSs: 100</li> <li>the number of total time sampling: 4s</li> </ul> </li> <li>figure1-11: <ul> <li>the dataset relevant to the plot of figures. </li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.