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781 results for “Earthquake”
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)
<p>Supplementary material for Sawi et al., 2023, <i>Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms </i>(The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. </p><p> </p><p><strong>Catalog Header</strong></p><p>YR/MO/DY...........Date of event</p><p>HR/MN/SC...........Time of event</p><p>LAT/LON/DEP........Location of event</p><p>EX/EY/EZ...........Relative location uncertainty (in m)</p><p>MAG................NCSN magnitude</p><p>evID.................NCSN event ID</p><p>seqID................Repeating earthquake sequence ID</p><p>isRESp............Is quasi-periodic RES (bool)</p><p> </p><p><strong>References: </strong></p><p>Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. </p><p>Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. <a href="https://doi.org/10.1029/2021JB022495">https://doi.org/10.1029/2021JB022495</a></p>
Catalog of NE Italy earthquakes Mw with related velocimetric time series
<p>Mw catalog (xlsx format) of earthquakes occurred in Norheastern Italy from 2016 to 2023; the catalog reports estimations for:</p> <ul> <li>ML (Bragato and Tento, 2005);</li> <li>Mw calculated from SA (Moratto et al., 2017);</li> <li>Mw calculated from MT (Moment Tensor; Saraò et al., 2021);</li> <li>The tgz file with the corrected velocimetric waveforms (SAC fomat with P and S arrival times used for the locations and units in m/s); tgz file can be found in Waveforms.tgz. EVDP SAC header is expressed in meters.</li> </ul> <p>Continuous raw time series can be dowloaded from Oasis website (Priolo et al., 2015).</p> <p> </p>
6C dataset for Mw 7.4 Hualien earthquake on 2024-04-02 at station locations MDSA0 and NA01.
<p>This repository contains the 6-component data set recording the Hualien Mw 7.4 earthquake on 2024-04-02. It contains the data for two station locations MDSA0 and NA01. For both locations there are 3 component rotation rates and 3 component accelerations. The instrument type and the coresponding response is in the file instrument_response.txt. It also includes the coordinates of both stations.</p> <p>After removing the response the acceleration data will be in units 'm/s/s' and the rotation data will be in 'rad/s'. </p> <p>The MDSA0 station is situated in Hualien, Taiwan. One blueSeis-3A rotational sensor is collocated with a Nanometrics Titan accelerometer at depth of 0.5 m in a vault structure.</p> <p>The NA01 station is situated in Nanao, Taiwan. One blueSeis-3A rotational sensor is collocated with a Kinemetrics EpiSensor accelerometer at depth of 2 m in a vault structure.</p>
Mechanical data of rotary shear fluid pressurised experiments for the manuscript: "Fluid pressurisation and earthquake propagation in the Hikurangi subduction zone"
<p>Mechanical data of rotary shear fluid pressurised experiments.</p> <p>Tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Downstream Pore Pressure: Pressure_ds (MPa)</li> <li>Confining Pressure: Pressure_conf (MPa)</li> <li>Upstream pore pressure: Pressure_us (MPa)</li> <li>Temperature of the upstream boundary of the gouge layer: Temperature_us (°C)</li> <li>Thickness of the gouge layer: Thickness (mm).</li> </ul>
Seismic moment tensor solutions of Mw > 3.4 earthquakes occurred between 2002 and 2023 in the Southeastern Alps
<p>Seismic moment tensor solutions of 63 earthquakes with 3.4≤ Mw≤ 5.1 occurring from 2002 to 2023 in the Southeastern Alps and strict surroundings (latitude 45°N-47.5°N and longitude 10°E-15°E). The seismograms have been recorded and acquired by the OGS - North-Eastern Italy Seismic and Deformation Network (<a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>). </p> <p>For more details:</p> <p>Saraò A., Sugan M., Bressan G., Renner G., and Restivo A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021.</p> <p> </p>
Deep learning-based earthquake catalog of the 2022 MW 6.9 Chihshang, Taiwan, earthquake sequence
<p>On 18 September 2022, the MW 6.9 Chihshang earthquake struck the southern Longitudinal Valley, Taiwan. We use SeisBlue, a deep-learning platform/package, to extract the two-month earthquake sequence from September to October 2022, including the MW 6.5 Guanshan foreshock, the MW 6.9 mainshock, over 14,000 aftershocks, and 866 focal mechanisms from two sets of broadband networks. For more details, please refer to our research article published at TAO (Sun et al., 2024; https://doi.org/10.1007/s44195-024-00063-9). The refined SeisBlue earthquake, FMS, and 20-year M3+ relocated CWA earthquake catalogs obtained in this study are listed here.</p> <ol> <li>The refined, deep-learning-based earthquake catalog of the 2022 Mw 6.9 Chihshang, Taiwan, earthquake sequence contains 5,151 seismic events with event time, location and error information, local and moment magnitudes, and hypoDD location. </li> <li>The FMS (focal mechanism solution) catalog is obtained by the P-wave polarities of 14 broadband stations and the FPFIT program (Reasenberg & Oppenheimer, 1985). 865 out of 1629 FMSs with at least six readings of P-wave polarity, F-fator <span>≤ </span>0.1 (F <span>< </span>0.5 for a good fit), and errors of strike, dip, and rake are all <span>< </span>20<span>°</span>, respectively, are listed in the attached FMS catalog.</li> <li>The 2001-2020 3D-hypoDD-relocated M3+ CWA earthquake catalog: We applied the HypoDD program (Waldhauser & Ellsworth, 2000) to the CWA (Central Weather Administration (CWA, Taiwan), 2012) catalog with P- and S-wave arrivals and obtained 5862 M3+ events between 2001 and 2020 for eastern Taiwan. The 3D velocity models used for this catalog are the local models from Kuo-Chen et al. (2012).</li> </ol> <p> </p>
Calibrated Earthquake Relocations from the TexNet Catalog (2017–2022) and Vertical Surface Deformation (2016–2022)
<p>This repository contains the relocated earthquake catalog for the Southern Delaware Basin, as presented in the manuscript titled "<em><strong>Insights into Spatiotemporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TexNet Catalog (2017-2022)</strong>".</em></p> <h3>Citations:</h3> <p>Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis; <strong>Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations <em>from the TexNet Catalog (2017-2022</em>) (2025)</strong>. <em>Earth and Space Science, 12 (6), e2024EA004027. <a title="https://doi.org/10.1029/2024EA004027" href="https://doi.org/10.1029/2024EA004027"><strong>https://doi.org/10.1029/2024EA004027</strong></a></em></p> <p>The earthquake relocations were conducted using the Hypocentroidal Decomposition technique with the <strong>open-source MLOC code</strong>, achieving enhanced spatial resolution for over 5,000 events from the TexNet catalog. The relocated catalog includes critical hypocentral parameters—latitude, longitude, depth—as well as origin time, associated uncertainties, and magnitude for each event.</p> <p>This dataset is an essential resource for analyzing the spatiotemporal patterns of induced seismicity associated with anthropogenic activities, such as shallow fluid injection, in the Southern Delaware Basin following the operation of TexNet in 2017. It is suitable for use in seismic hazard assessments, modeling studies, and comparisons with other induced seismicity datasets. Additional data produced during this research includes vertical surface deformation measurements from 2016 through the end of 2022.</p> <p>The repository also contains data referenced in the manuscript’s “Data Availability Statement” and “Open Research” sections. </p> <p>List of files attached to this repository:</p> <ul> <li><strong>catalog.xls</strong>: Primary earthquake relocated catalog developed in this study</li> <li><strong>2016_2022_deformation.csv</strong>: Vertical displacement data (2016–2018) developed in this study</li> <li><strong>2019_2022_deformation.csv</strong>: Vertical displacement data (2016–2022) developed in this study</li> <li><strong>post-2017-injection.xlsx</strong>: Injection data from the Railroad Commission of Texas (<a href="https://www.rrc.texas.gov">source</a>)</li> <li><strong>Hydrofracking-post2017.xlsx</strong>: Hydrofracking well data from FracFocus (<a href="https://fracfocus.org">source</a>)</li> <li><strong>GrowClust-common.xls</strong>: 2-D GrowClust catalog for supplemental information (<a href="https://hirescatalog.texnet.beg.utexas.edu/">source</a>), https://doi.org/10.15781/76hj-ed46</li> <li><strong>TexNet-Catalog</strong>: Initial TexNet catalog's origin and phase data, https://doi.org/10.7914/SN/TX</li> </ul>
Updated NLL-SSST-coherence earthquake relocation catalog for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.
<p>CSV catalog and plots of NLL-SSST-coherence earthquake relocations of 17583 events M≥1 2020.01.01 to 2021.08.20 for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.</p> <p>This dataset is an update of the relocations presented in the pre-print article:<br> The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system<br> https://eartharxiv.org/repository/view/1904/</p> <p> </p> <p>Plots show map, view from south and view from N73E of NLL-SSST-coherence relocations events with error-ellipsoid, semi-major axis ≤ 5 km. Caption (adapted from https://eartharxiv.org/repository/dashboard/1904/ ; Fig 2):</p> <p>Event color showing hypocenter depth and symbol size proportional to magnitude. Re-picked Mw6.5 hypocenter and its proxy (mean hypocenter of 3 well constrained foreshocks) indicated by small and large, dark red, cross symbols, respectively. SHmin and SHmax show directions of regional minimum and maximum compressive stress, respectively; the intermediate principal stress axis is vertical. Seismic stations shown as dark gray tetrahedrons. Brown lines show faults from the Quaternary fault and fold database for the United States. Background topography image from OpenTopgraphy.org. Red lines show mapped surface ruptures from http://www.nbmg.unr.edu/Geohazards/Earthquakes/MonteCristoRangeEQData.html and https://doi.org/10.1785/0220200371</p>
NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.
<p>CSV tables of the final, NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.</p> <p>These datasets are from relocations presented in the article<br> High-precision, earthquake location using source-specific station terms and inter-event waveform similarity<br> submitted to Journal of Geophysical Research Solid Earth</p> <p> </p> <p> </p>
Third Uniform California Earthquake Rupture Forecast (UCERF3) Fault System Solutions
<p>Data files for the Third Uniform California Earthquake Rupture Forecast (UCERF3), as described in <a href="https://doi.org/10.1785/0120130164">https://doi.org/10.1785/0120130164</a>.<br> <br> These data are stored in the original UCERF3 Fault System Solution file format, which uses binary files within zip containers. This format is being revised, and updates to this dataset will be published when the new and more user friendly format is finalized. See <a href="https://opensha.org/File-Formats">https://opensha.org/File-Formats</a> for more information.<br> <br> File descriptions:<br> <br> <strong>Branch Averaged Files</strong></p> <p>These files contain branch-averaged fault system solutions, where rupture properties (magnitude, rake, rate of occurrence, etc) are averaged across all UCERF3 logic tree branches, according to each branch's weighting in the final model. This is the simplest version of the model, and can be used as a quick approximation to mean hazard. One file exists for each fault model, and these files are compatible with the time-dependent version of UCERF3.</p> <ul> <li><em>branch_averaged_ucerf3_sol_FM3_1.zip</em> - fault model 3.1 branch averaged fault system solution</li> <li><em>branch_averaged_ucerf3_sol_FM3_2.zip</em> - fault model 3.2 branch averaged fault system solution</li> </ul> <p><strong>Full Model (Compound Solutions)</strong></p> <p>These files contain the full UCERF3 logic tree, and can be used to extract data for individual logic tree branches (e.g., for use in hazard calculations that consider all epistemic uncertainties).</p> <ul> <li><em>full_ucerf3_compound_sol.zip</em> - full compound solution file with information on all 1,440 time-independent logic tree branches</li> <li><em>full_ucerf3_compound_sol_with_individual_runs.zip</em> - same as above, but also containing rates for each of 10 simulated annealing inversion runs for each logic tree branch (total of 14,400 inversions)</li> </ul> <p><strong>True Mean Solutions</strong></p> <p>A different type of branch averaged solution, the “true mean” solution, is also available. They are similar to the branch averaged fault system solution described above, but instead use duplicate versions of each rupture whenever a key property (rake, magnitude, area) changes. This retains all variability allowing for quick reproduction of mean UCERF3 results with a minimum set of ruptures. The MeanUCERF3 ERF implemented in <a href="https://opensha.org">OpenSHA</a> uses these files and also allows the user to apply various approximations to further reduce the rupture count.</p> <p>Note: These solutions are not compatible with time dependent UCERF3 calculations as multiple instances of each subsection may exist, resulting in rate partitioning between instances and incorrect recurrence intervals for renewal model calculations.</p> <ul> <li><em>true_mean_ucerf3_sol.zip</em> - true mean fault system solution, across both fault models</li> <li><em>true_mean_ucerf3_sol_FM3_1.zip</em> - true mean fault system solution, only for fault model 3.1</li> <li><em>true_mean_ucerf3_sol_FM3_2.zip</em> - true mean fault system solution, only for fault model 3.2</li> </ul> <p><strong>Metadata</strong></p> <p>A copy of the original file format description is included in <em>file_format.md</em>, and is also <a href="https://opensha.org/File-Formats">available online here</a>. A CSV file that includes information on each gridded seismicity location is also included (<em>relm_gridded_region.csv</em>).</p>
Setup of the 2010 Darfield earthquake for SeisSol
<p>Dynamic rupture setup of the 2010 Darfield earthquake built for SeisSol (https://github.com/SeisSol/SeisSol/).</p> <p><strong>SeisSol version</strong></p> <p>This setup is built for SeisSol version 479583792e651bf18bd582f71254722ee023ca07.<br> Fault segments are distinguished by using the fault tagging feature<br> (see https://seissol.readthedocs.io/en/latest/fault-tagging.html). In essence,<br> boundary conditions 3, 7, 8, 9, 10, 11, and 12 are all interpreted as fault and<br> each number marks a fault segment. However, fault tagging is experimental and<br> this behaviour might change in future versions of SeisSol. In case the fault tags<br> need to be adjusted, make sure to edit meshing_options.xml and re-generate the mesh,<br> and edit initial_stress.yaml.</p> <p><strong>Projection</strong></p> <p>EPSG:32759</p> <p><strong>File list</strong></p> <p>- Darfield.h5<br> Mesh with 98113814 elements.<br> - fault_cad_model.geo<br> CAD model of the fault geometry for Gmsh.<br> - initial_stress.yaml<br> material.yaml<br> fault.yaml<br> Material model and fault parameterisation with easi.<br> - meshing_options.xml<br> Meshing options to generate the mesh with PUMGen (https://github.com/SeisSol/PUMGen/tree/xml).<br> - Qp.nc<br> Qs.nc<br> vel_model.nc<br> 3D material model on uniform grid (lambda, mu, rho, Qp, Qs).<br> - roughness0.nc<br> roughness1.nc<br> roughness2.nc<br> roughness3.nc<br> roughness4.nc<br> roughness5.nc<br> Relative pre-stress ratio for the six fault segments.<br> - Darfield.smd<br> CAD model with topography. Opens with SimModeler from Simmetrix and is passed to<br> PUMGen for mesh generation.<br> - parameters.par<br> Settings for invoking SeisSol.<br> - receivers.txt<br> Position of synthetic seismographs.</p> <p> </p>
Dataset for "Partitioned fault movement and aftershock triggering: evidence for fault interactions during the 2017 Mw 5.4 Pohang earthquake, South Korea"
<p>This repository contains the seismograms of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and the Korea Institute of Nuclear Safety (KINS) used in Son et al. (2020). The uploaded waveforms were filtered according to the Supporting Information of Son et al. (2020). Continuous waveforms are available via the Korea Meteorological Administration (KMA; http://necis.kma.go.kr).</p> <p>Suggested citation: Son, M., Cho, C. S., Lee, H. K., Han, M., Shin, J. S., Kim, K., Kim, S. (2020). Partitioned fault movement and aftershock triggering: evidence for fault interactions during the 2017 Mw 5.4 Pohang earthquake, South Korea. Journal of Geophysical Research: Solid Earth, e2020JB020005. <a href="https://doi.org/10.1029/2020JB020005">https://doi.org/10.1029/2020JB020005</a></p>
Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"
<p>Mechanical data of rotary shear experiments and temperature measurements</p> <p>Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber).</p> <p>Mechanical data are presented in a tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul> <p> In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>
Data pertaining to 'Can artesian groundwater and earthquake-induced aquifer leakage exacerbate the manifestation of liquefaction?'
<p>Geodatabase (ArcGIS 10.5.1), Excel files and mp4 video to accompany: </p> <p>Cox, S.C.; van Ballegooy, S.; Rutter, H.K.; Harte, D.S.; Holden, C.; Gulley, A.K.; Lacrosse, V.; Manga, M. (in press) Can artesian groundwater and earthquake-induced aquifer leakage exacerbate the manifestation of liquefaction? Engineering Geology Manuscript ENGEO 105982.</p> <p>Vast quantities of liquefaction ejecta repeatedly inundated properties during the 2010-2011 Canterbury earthquake sequence in New Zealand, resulting in differential ground surface subsidence and significant damage to buildings and urban infrastructure. There are strong spatial correlations between the occurrence of ejected sediment with groundwater pressure in deep aquifers. When geotechnical testing sites are grouped according to liquefaction vulnerability indices (to control variance relating to shaking strength, water table depth, and soil profile strength), places where ‘minor’ and ‘moderate-severe’ liquefaction occurred during the Mw6.2 Christchurch earthquake had distinctly higher aquifer pressure than sites where liquefaction was not observed. Together with observations of earthquake-induced pressure changes and inferred transfer of groundwater from deep aquifers to shallower levels, an interpretation is that leakage from aquifers with artesian (above ground) pressure provided an additional driving mechanism for surface manifestation of water and sediment. It is surmised that above-ground aquifer pressures further promoted suffusion and piping along fractures, flow-pathways and liquefied horizons. The Mw6.2 Christchurch earthquake is presented as an example where leakage of artesian groundwater likely contributed to the near-surface liquefaction-induced ground damage. The process can result in underprediction of liquefaction vulnerability so needs to be considered when evaluating potential for earthquake-induced liquefaction and ground damage wherever groundwater is confined.</p>
Aftershock data for the 2017 MW5.5 Pohang earthquake
<p><strong># written by J.-U. Woo</strong><br> Data includes [1] initial earthquake catalog and [2] relocated earthquake catalog</p> <p><strong>Suggested citations:</strong><br> (For the uploaded catalogs) <strong>1.</strong> J.-U. Woo, M. Kim, J. Rhie, and T.-S. Kang, 2020, Aftershock sequence and statistics of the 2017 MW 5.5 Pohang earthquake, South Korea: implications of fault heterogeneity and post-seismic relaxation, Bulletin of Seismological Society of America. Detailed information will be posted on the zenodo website.</p> <p><strong>Further suggested citations:</strong><br> (For 1-D velocity model at EGS site and catalogs at Pohang EGS site) <strong>2.</strong> J.-U. Woo, M. Kim, D.-H. Sheen, T.-S. Kang, J. Rhie, F. Grigoli, W.L. Ellsworth, D. Giardini, 2019, An In-Depth Seismological Analysis Revealing a Causal Link Between the 2017 MW 5.5 Pohang Earthquake and EGS Project, JGR solid earth, doi:10.1029/2019JB018368.<br> <strong>3.</strong> W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, and T. Shimamoto, 2019, Triggering of the Pohang, Korea, Earthquake (Mw 5.5) by Enhanced Geothermal System Stimulation. Seismological Research Letters, 90(5), 1844-1858.<br> <strong>4.</strong> K.-K. Lee, W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, T. Shimamoto, I.-W. Yeo, T.-S. Kang, J. Rhie, D.-H. Sheen, C. Chang, J.-U. Woo, C. Langenbruch, 2019, Managing injection-induced seismic risks. Science, 364(6442), 730-732.<br> <strong>5.</strong> C. Langenbruch, W. L. Ellsworth, J.-U. Woo and D. J. Wald, 2020, Value at Induced Risk: Injection-induced seismic risk from low-probability, high-impact events. Geophysical Research Letters, 47, e2019GL085878. http://doi.org/10.1029/2019GL085878.</p> <p><strong>Descriptions:</strong><br> [1] Initial earthquake catalog (cat1)<br> - First column: event index (sequentially increasing number)<br> - Second column: event ID<br> - Third column: event origin date as year/month/day_of_month<br> - Fourth column: event origin time as hour:minute:second<br> - Fifth and sixth columns: latitude and longitude in degree<br> [2] Relocated earthquake catalog (cat2)<br> - First column: event index (linked with event index of cat1)<br> - Second column: event ID (linked with event ID of cat2)<br> - Third column: event origin date as year/month/day_of_month<br> - Fourth column: event origin time as hour:minute:second<br> - Fifth and sixth columns: latitude and longitude in degree<br> - Seventh column: depth in km<br> - Eighth column: 1.96*(standard deviation of W-E relative locations obtained from 200 times of bootstrap resampling) in meter<br> - Ninth column: 1.96*(standard deviation of S-N relative locations obtained from 200 times of bootstrap resampling) in meter<br> - Tenth column: 1.96*(standard deviation of relative depths obtained from 200 times of bootstrap resampling) in meter</p>
3D CMT catalogue of moderate size offshore earthquakes along the Nankai Trough
<p>3D CMT inversion solutions of moderate-size earthquakes along the Nankai Trough, <strong>version 3.1. </strong> </p> <ul> <li>Analyzed periods: <strong>January 2003 to December 2020</strong> <ul> <li>The catalog version 3, containing CMT solutions from January 2003 to April 2020.</li> <li>The catalog version 2.2, which is containing CMT solutions from April 2004 to August 2019, has been published in GJI (Takemura, Okuwaki et al. 2020 <a href="https://doi.org/10.1093/gji/ggaa238">doi:10.1093/gji/ggaa238</a>).</li> </ul> </li> <li>The method is described in Takemura, Okuwaki, et al., 2020, GJI, <a href="https://doi.org/10.1093/gji/ggaa238">doi:10.1093/gji/ggaa238</a> <a href="https://doi.org/10.31223/osf.io/nbd79">the submitted preprint</a>. </li> </ul> <p>If you use this version, you should cite the appropriate DOI and Takemura, Okuwaki, et al. 2020 GJI.</p> <p><strong>Included files</strong></p> <ul> <li>YYYYMMDDHHMM_25-100s__CMT.dat<br> CMT solutions at all selected source grids for an earthquake that occurred at HH:MM on DDth MM YYYY (JST). Latitude, longitude, depth, VR [%], M<sub>rr</sub>, M<sub>tt</sub>, M<sub>ff</sub>, M<sub>rt</sub>, M<sub>rf</sub>, M<sub>tf</sub>, exponent (dyne-cm), Mo [Nm], strike1, dip1, rake1, strike2, dip2, rake2, Mw, index of source grid (internal parameter), and centroid time are listed. </li> <li>YYYYMMDDHHMM_25-100s__CMTparam.dat<br> Input directory (internal parameter), Green's function directory (internal parameter), the number of source grids, the number of used stations, station names used in CMT inversion, frequency range, initial epicenter and distance range are listed.</li> <li>3DCMTcatalog_v3.csv<br> CSV format file of the 3D CMT catalog for earthquakes with Mw of 4.3-6.5</li> <li>catalog3DCMT_Takemura2019_Mw7.2_7.5SEKii.csv<br> CSV format file of the 3D CMT catalog for the Mw 7.2 and 7.5 southeast off the Kii Peninsula earthquake occurred on 19:07 and 23:57 5th September 2004 (JST), respectively.</li> </ul>
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
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>
Differential Interferogram of the Lefkada 17 November 2015 Earthquake
<p>On the 17th of November 2015, an earthquake of Mw 6.4 hit the western Greek island of Lefkada, located in Ionian Sea, an area that is well known for its active tectonics. A second earthquake of Mw 5.0 successively followed. These events induced rock falls and landslides having as consequences two life losses and extensive damages to roads and buildings.<br /> Shortly after the events, BEYOND acquired a set of Sentinel-1 TOPSAR scenes, one before and one after the event. The images were combined to form an interferogram that depicts ground deformation due to the earthquake events.<br /> The fringes of the preliminary interferometric results, which are under further elaboration, reveal ground deformation of the order of ~20cm along the Line of Sight at the western part of Lefkada Island. As smaller deformation field is also apparent at the northern part of Cephalonia Island.</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)
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.