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662 results for “Seismicity”
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
Gravity modeling of the Alpine lithosphere affected by magmatism based on seismic tomography
<p>The Southern Alpine regions have been affected by several magmatic and volcanic events between the Paleozoic and the Tertiary. This activity has undoubtedly had an important effect on the density distribution and structural setting at lithosphere scale. Combining the information from gravity field and a high-resolution seismic tomography has been carried out a new 3D lithosphere density model of the Alpine region.</p>
DETOX seismic tomography models
<p>-----------------------<br> DETOX tomography models<br> -----------------------</p> <p>This folder contains three tomography models, DETOX-P1, DETOX-P2 and DETOX-P3, in the following formats: </p> <p>- NetCDF (dirname: grid_nc4)<br> - VTK (dirname: vtk)<br> - xyz-value (dirname: txt_tetrahedron)<br> - JPEG for GPLATES, only high-velocities (dirname: GPLATES)</p> <p>The directories are organized as follow:<br> <br> DETOX-P1<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk<br> DETOX-P2<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk<br> DETOX-P3<br> ├── GPLATES<br> ├── grid_nc4<br> ├── txt_tetrahedron<br> └── vtk</p> <p>---------------------</p> <p>Citation:</p> <p>* Kasra Hosseini, Karin Sigloch, Maria Tsekhmistrenko, Afsaneh Zaheri, Tarje Nissen-Meyer, Heiner Igel, Global mantle structure from multifrequency tomography using P, PP and P-diffracted waves, Geophysical Journal International, Volume 220, Issue 1, January 2020, Pages 96–141, https://doi.org/10.1093/gji/ggz394</p>
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface
<p>Video-documentation of the <strong><em><a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a></em> Volcano-Independent Seismic Recognition (VI.VSR) software</strong>, supported by the <a href="https://cordis.europa.eu/project/id/749249"><strong><em>VULCAN.ears</em></strong></a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the <em>"Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - </em>(Cortés et al., Frontiers in Earth Sciences, 2021) article. <em><strong>VI.VSR aim</strong></em> is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios.</p> <p>The material includes 2 VDs:</p> <ol> <li><em>"VI.VSR+geoStudio_intro.mp4"</em> -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting <em>geoStudio</em> and its role in the whole <em>VULCAN.ears</em> platform.</li> <li><em>"VI.VSR.by.geoStudio_case.studies.mp4"</em> -> running the VI.VSR case studies presented in the <em>(Cortés et al., 2021)</em> manuscript.</li> </ol> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography
<p>We provide: the tomographic model for dephts 68 to 768 km as text files, where the first column is the longitude, the second is the latitude and the third if the velocity perturbation; the proposed limits for the Paranapanema Block, Luiz Alves Craton and Rio Apa Craton as a csv file (Figure 12 of the paper), where the first column is the name of the feature, the second is the longitude and the third is the latitude; and the abstract for the paper "Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography".</p>
L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations
<p>This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy).</p> <p>It contains:</p> <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag <strong>fty</strong> in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 M<sub>L</sub> >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag <strong>fty</strong> in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes M<sub>L</sub> >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag <strong>fty</strong> in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> <p>The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database. </p> <p>Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>OT_Date:</strong> date of the origin time in the format YYYY-MM-DD</p> <p><strong>OT_Time:</strong> time of the origin time in the format HH:mm:ss.dcm</p> <p><strong>lat:</strong> hypocenter latitude expressed in degrees </p> <p><strong>lon:</strong> hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep:</strong> hypocenter depth expressed in km </p> <p><strong>ML:</strong> local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)</p> <p> </p> <p><strong>id_dd:</strong> the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset</p> <p><strong>IMPORTANT NOTE about st1 and st2 (below): </strong>the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space</p> <p><strong>st1:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 1 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 1 (FPFIT convention)</li> </ul> <p><strong>dip1: </strong>dip of plane 1</p> <p><strong>rk1: </strong>rake of plane 1</p> <p><strong>st2:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 2 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 2 (FPFIT convention)</li> </ul> <p><strong>dip2: </strong>dip of plane 2</p> <p><strong>rk2: </strong>rake of plane 2</p> <p><strong>fty:</strong> flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP</p> <p><strong>MW:</strong> only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a></p>
High Speed Rail Seismic Observation in Baoding, Hebei Province of China
<p>H5 files includes all train events collected in the observation. Raw data in sac format is too big (600GB) to upload.</p> <p>To access all continuous data, please contact shiyxg@mail.iggcas.ac.cn/wenjc@pku.edu.cn/njy@pku.edu.cn</p> <p> </p> <table> <tbody> <tr> <td>File names</td> <td>Format</td> <td>date</td> </tr> <tr> <td>BSPK095*</td> <td>-100s-100s, dt=0.01</td> <td>0424-0504</td> </tr> <tr> <td>BSPKU87*</td> <td>-100s~100s, dt=0.01</td> <td>0510-0518</td> </tr> <tr> <td> <p>hsr_coor_stacked_201804*</p> </td> <td>stacked traces in different frequency bands</td> <td>0424-0504</td> </tr> <tr> <td> <p>hsr_coor_stacked_201805*</p> </td> <td>stacked traces in different frequency bands</td> <td>0510-0518</td> </tr> <tr> <td> <div>coor_all_201804_YNPK_CZ_158.npy</div> </td> <td> <p>ambient noise results at night, between 158 stations</p> <p>(S158_1804.txt)</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>coor_all_201805_YNPK_CZ_143.npy</div> </td> <td> <p>ambient noise results at night, between 143 stations</p> <p>(S143_18045txt)</p> </td> <td>0510-0518</td> </tr> <tr> <td> <div>1804_YNPK_CZ_f0.2_20_all_night.h5</div> </td> <td> <p>Continuous data at 13 nights of 4 stations for stability comparsion</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>H1.h5</div> </td> <td> <p>Correlation results of array in Baoding, 2023 March.</p> </td> <td>2023/0311-0328</td> </tr> </tbody> </table>
UAV-derived DEM and DOM along the co-seismic surface ruptures produced by the Mw5.7 aftershock during the 22-01-2024, Mw7.0 Wushi earthquake, Xinjiang, China
<p>This unmanned aerial vehicle (UAV) dataset was acquired by a DJI Matrice 300 RTK and a DJI Phantom 4 Pro, on February 3 and 5, 2024, respectively. The Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM) were processed using the Agisoft Metashape Professional software. These data were used to map the co-seismic surface ruptures produced by the 29-01-2024, Mw5.7 aftershock following the 22-01-2024 Mw7.0 Wushi mainshock, Xinjiang, China, and to measure the associated vertical offsets along the surface ruptures.</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>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
Beirut blast seismic records and Rinex data of CORS-TR DOY217 of 2020
<p>These are the seismic data and the Rinex observation data for the DOY 217 (August 4, 2020), the day of the explosion of Beirut Port. The data was used in the results and analysis of a manuscript entitled with "Investigation of the Lithosphere-Atmosphere-Ionosphere Coupling during Beirut Explosion, Lebanon, by Geodetic and Seismological data"</p>
Southern South Island, New Zealand, seismic observations and velocity model for Tectonics 2021TC007006
<p>This archive has data and results from the Tectonics paper, “The influence of basement terranes on tectonic deformation: joint earthquake travel-time and ambient noise tomography of the southern South Island, New Zealand” (Eberhart-Phillips et al., 2022; doi:10.1029/2021TC007006). That work incorporated earthquake observations from the 2014-2015 Otago temporary broadband network. Group velocity observations are in ‘groupvel_obs_otaf_T5_T9.vgsw’. We used earthquakes observed on the COSA temporary network. The Otago-COSA travel-times and hypocenters are in ‘tt_archive.tar’. The Otago earthquakes were evaluated within Seiscomp. Those observation files, which include polarity, are in ‘polarity_bulletin.tar’. We did not use the polarities in the Tectonics paper, but we archive them here for completeness. The waveform data will be available from IRIS.</p> <p>The southern South Island 3-D velocity model is provided in the table ‘vlotf30xyzltlnDWSSF.mod’, with DWS (derivative weight sum) describing the distribution of data, and SF (spread function) describing the resolution averaging. Later on, this will be merged into the New Zealand wide velocity model version 2.3, which will be placed on Zenodo.</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Legacy seismic data fo the 1928 Parral, Mexico earthquake (M6.3)
<p>This data set is part of the 01/11/1928 Parral, Mexico earthquake (M6.3)</p> <p>Includes records from the 1928 National Seismological Service SSN) network, recorded on Wiechert seismographs smoked paper, as well as records from the California network Caltech archive.</p> <p> </p>
Combining Horizontal Strain DAS and Local Seismic Stations in a Full Waveform Attribute Stacking Detector/Locator Algorithm: Verification Test for the Thorbjörn, Iceland, 2020 Unrest Episode
<p>We present a waveform stacking-based earthquake catalog of the seismicity unrest episode in the Svartsengi fissure swarm close to Mt. Thorbjörn, SW Iceland, which started in January 2020 and was still ongoing in January 2021. The magmatic unrest produced more than 5 earthquake swarms comprising thousands of individual events each. We were able to combine local and regional seismic networks with 6 months recording of a 17 km long distributed acoustic sensing (DAS) fibre optical cable with a channel resolution of 4 m. The kHz DAS data were downsampled to 200 Hz and stacked every 64 m. The catalog is based on a migration-based detector / locator technique as for instance implemented in Lassie (Pyrocko). In the accompanying we demonstrate the robustness in a wide variety of applications in seismology. For this dataset, we have extended Lassie to efficiently combine linear ultra-dense sensor arrays with sparse seismological networks.</p>
Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure
<p>These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).</p> <p> </p>
Relocated Seismicity Catalogs on the Discovery Transform Fault, 4S on the East Pacific Rise
<p>Two relocated earthquake catalogs are provided for the Discovery Transform Fault located at 4ºS on the East Pacific Rise. There is a microseismicity catalog representing one year of activity recorded during a 2008 ocean bottom seismometer deployment, which includes 12,635 events with local magnitudes, M<sub>L</sub>, between 0 and 4.1. The second catalog includes 24 years (1 January 1990 - 1 April 2013) of earthquakes obtained from the global Centroid Moment Tensor (CMT) catalog, a total of 15 events, with seismic moment magnitudes, M<sub>W</sub>, between 5.4 and 6.0.</p> <p>Microseismicity was relocated using the HypoDD relocation algorithm (Waldhauser, 2001), while the CMT events were relocated using a teleseismic surface-wave cross-correlation technique (McGuire, 2008). The 15 CMT events all relocated into one of five distinct rupture patches on the Discovery Transform Fault. In general, microseismicity was found to be reduced within these large, repeating rupture patches.</p> <p>A more detailed description of the methodology used to relocate both catalogs, as well as a discussion on the correlation between seismic behavior and fault structure on the Discovery Transform Fault is provided in:</p> <p>Wolfson-Schwehr, M., Boettcher, M. S., McGuire, J. J., & Collins, J. A. (2014). The relationship between seismicity and fault structure on the Discovery transform fault, East Pacific Rise. <em>Geochemistry, Geophysics, Geosystems, </em>15(9), 3698–3712. <a href="https://doi.org/10.1002/2014GC005445">https://doi.org/10.1002/2014GC005445</a></p> <p>Seismic Catalogs:</p> <ul> <li>Discovery_CMT_relocated_seismicity_1990_2013.csv</li> <li>Discovery_relocated_microseismicity_2008.csv</li> </ul> <p>Additional References:</p> <p>1. McGuire, J. J. (2008). Seismic cycles and earthquake predictability on East Pacific Rise transform faults. <em>Bulletin of the Seismological Society of America</em>, 98(3), 1067-1084. <a href="https://www.whoi.edu/cms/files/McGuire_BSSA_2008_48643.pdf">https://www.whoi.edu/cms/files/McGuire_BSSA_2008_48643.pdf</a></p> <p>2. Waldhauser, F. (2001). hypoDD--A program to compute double-difference hypocenter locations. <br> <a href="https://academiccommons.columbia.edu/doi/10.7916/D8SN072H">https://academiccommons.columbia.edu/doi/10.7916/D8SN072H</a></p>
Seismic dataset. Mt. Etna, November, 2013.
<p>We present a seismic dataset recorded during November 2013 by a 3 components seismic station, called EBEM, belonging to the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo monitoring network. This dataset is part of a researcher paper <em>“A successful short-term volcanic eruption forecasting using seismic features”</em>. </p> <p>This data repository includes continuous raw waveforms (Z component) in SAC format, and related metadata. The seismic station EBEM was located at an elevation of about 2700 m a.s.l., at a distance of about 1500 m from the South-East Crater.</p> <p>The EBEM station was equipped with a Lennartz LE-3D/20s (T=20s) 3 components, with flat frequency response of 0.05 - 50 Hz (<a href="https://www.nanometrics.ca/products/seismometers">https://www.lennartz-electronic.de/wp-content/uploads/2021/04/Lennartz-SeismometerManual.pdf</a>).</p> <p>Data were sampled at 100 Hz using Nanometrics Trident digital data recorders (https://www.nanometrics.ca). Trident recorders have a resolution of 24 bit (at 100 Hz), and a GPS timing accuracy of <100 microseconds to UTC.</p> <p> </p> <p> </p> <p>Acknowledgment:</p> <p>This dataset is part of the paper <em>“A successful short-term volcanic eruption forecasting using seismic features”</em>, that was partially supported by the Spanish FEMALE project (PID2019-106260GB-I00). P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN), Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE), and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719). Ivan Koulakov was supported by the Russian Science Foundation (Grant No. 20-17-00075). Luciano Zuccarello was supported by the INGV Pianeta Dinamico 2021 Tema 8 SOME project (grant no. CUP D53J1900017001) funded by the Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018”.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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