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781 results for “earthquakes”
Earthquake Nucleation Size: Evidence of Loading Rate Dependence in Laboratory Faults
<p>The data presented here is complementary to the manuscript 'Earthquake Nucleation Size: Evidence of Loading Rate Dependence in Laboratory Faults'.</p> <p>It comprises selected movies of rupture propagation at different loading rates as well as strain gages time signals filtered at 500 kHz and 30 kHz.</p> <p>Useful information about the processed data can be found in excel spreadsheets and text files given in the folders. Some python scripts are also available to plot the data.</p>
Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake
<p>We release two datasets that track the enhanced landsliding induced by the 2008 M<sub>w</sub> 7.9 Wenchuan earthquake over a portion of the Longmen mountains, at the eastern margin of the Tibetan plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris, and post-seismic landslides (new failures). It covers 471 km<sup>2</sup> in the earthquake’s epicentral area, from 2005 to 2018. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (~17,000 km<sup>2</sup>), together with information on their triggering rainfall as recorded by a network of rain gauges. For some well-monitored events, we provide more detailed data on rainfall, discharge, flow depth and density. The datasets can be used to analyse, at various scales, the patterns of landsliding caused by the earthquake. They can be compared to inventories relative to past or new earthquakes or other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets can be accessed from <a href="https://doi.org/10.5281/zenodo.1405490">https://doi.org/10.5281/zenodo.1405490</a>. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.</p>
Subpixel offsets of Copernicus Sentinel 2 data, related to the displacement field of the Sulawesi Earthquake (2018, Mw 7.5)
<p><a href="https://en.wikipedia.org/wiki/Sulawesi">Sulawesi</a> lies within a complex fault system located between the <a href="https://en.wikipedia.org/wiki/Australian_Plate">Australian</a>, <a href="https://en.wikipedia.org/wiki/Pacific_Plate">Pacific</a>, <a href="https://en.wikipedia.org/wiki/Philippine_Sea_Plate">Philippine</a> and <a href="https://en.wikipedia.org/wiki/Sunda_Plate">Sunda Plates</a>. The main active structure onshore at the western part of Central Sulawesi is the left-lateral NNW-SSE trending <a href="https://en.wikipedia.org/wiki/Palu-Koro_fault">Palu-Koro</a> <a href="https://en.wikipedia.org/wiki/Strike-slip_Fault">strike-slip fault</a> that forms the boundary between the North Sula and Makassar blocks. On 28 September 2018, a large tsunamigenic <a href="https://en.wikipedia.org/wiki/Earthquake">earthquake</a> (Mw 7.5) struck the <a href="https://en.wikipedia.org/wiki/Minahasa_Peninsula">Minahasa Peninsula</a>, Indonesia. The earthquake caused massive damages near Palu city, including onshore gravitational instabilities and a tsunami.</p> <p>These data are the result of subpixel image correlation on Copernicus Sentinel-2 data (17 September 2018 and 2 October 2018) to derive the two-dimensional (East-West and North-South) horizontal co-seismic displacement field. In these data, the displacement field is expressed in meters. These results show a dominant senextral strike-slip motion on the onshore part of the Palu-Koro fault. Maximum displacement at the surface reached more than 8 meters at the location of Palu city. Processing is performed with COSI-CORR (Leprince et al., 2007). Data value higher than | 10 | meters should be considered as noise and disregarded. I used a correlation window size of 32 pixels with a sampling step of 16 pixels. A ramp has been removed from (separately) the North-South offsets and from the East-West offsets. The files are rasters of floating point values, served with a header file readable by ENVI software.</p> <p>Sign conventions:</p> <p>-North-South offsets: positive values to the North.</p> <p>- East-West offsets: positive values to the West.</p> <p> </p> <p><a href="http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data">http://www.esa.int/spaceinimages/Images/2018/10/Indonesia_earthquake_displacement_data</a></p> <p><strong>Copyright:</strong> Contains modified Copernicus Sentinel data (2018), processed at the French Geological Survey (BRGM)</p> <p> </p> <p> </p>
Earthquake Early Warning Global Test Suite
<p>This is the companion data to the JGR paper "Quantifying the Value of<br> Real-time Geodetic Constraints on Earthquake Early Warning using a<br> Global Seismic and Geodetic Dataset" by Ruhl et al. It contains both<br> strong motion and GNSS displacement waveforms. There is one folder per<br> event, and for each there is an "accel" and a "disp" file containing<br> each kind of data. There is a .chan channel file with station metadata.</p> <p>Changes from Version 1.0:</p> <p>The timing of seismic waveforms for Cascadia001300 were delayed by 1<br> minute and this has been corrected to match the geodetic data and the<br> origin time.</p> <p>Four of the Japanese events were mistakenly in GPS time and have now<br> been corrected into UTC time to match the seismic data. Affected events<br> include Tohoku2011, Miyagi2011B, E.Fukushima2011, and Kumamoto2016.</p>
Subpixel optical correlation co-seismic offsets for the Mw 6.4 and Mw 7.1 Ridgecrest, California earthquakes, from Copernicus Sentinel 2 data
<p>Two strong earthquakes (Mw 6.4 and Mw 7.1) took place near Ridgecrest, California, on July 4 2019 and July 6, respectively.</p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive</a></p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive</a></p> <p>In order to assess surface ruptures and the displacement field from the earthquakes, we used subpixel image correlation with Copernicus Sentinel-2 optical imagery (Band 4). MicMac and CosiCorr software was used to to extract the 2D (East-West and North-South) horizontal co-seismic displacement field.</p> <p>Four high-resolution figures are given per method and component (EW and NS). Road network (white lines - from OpenStreetMap) and Quaternary Faults (black polylines) from USGS (<a href="https://earthquake.usgs.gov/hazards/qfaults/">https://earthquake.usgs.gov/hazards/qfaults/</a>) are used for overlay.</p> <p>Rasters are given per software used (MICMAC_ for MicMac and COSI for CosiCorr), with a pixel resolution of 20m. Final product is corrected with detrending (to remove mostly registration errors) and filtered to remove noise. Stripes resulting from pushbroom scanner and orbit errors were not removed at this product (visible as WNW-ESE and NNE-SSW linear parallel stripes).</p> <p>-North-South displacement: positive values to the North.</p> <p>- East-West displacement: positive values to the East.</p> <p>Raster files are projected in UTM Zone 11North WGS84 ( EPSG:32611)</p> <p> </p> <p>A contribution to <strong>CEOS Working Group Disasters:</strong> Seismic Demonstrator</p> <p><strong>Copyright:</strong> Contains modified Copernicus Sentinel data (2019), OpenStreetMap data (2019), Quaternary Fault and Fold Database of the United States - USGS (2019)</p>
Slip model and Relative Source Time Functions for the 2013 Mw 3.3 St. Gallen earthquake
<p>Slip model and Relative Source Time Functions for the the 2013 Mw 3.3 St. Gallen earthquake</p> <p>Király‐Proag, E., Satriano, C., Bernard, P., & Wiemer, S. (2019). Rupture process of the Mw 3.3 earthquake in the St. Gallen 2013 geothermal reservoir, Switzerland. Geophysical Research Letters, 46, doi: <a href="https://doi.org/10.1029/2019GL082911">10.1029/2019GL082911</a></p>
Supplement to Warwel et al. : Local Earthquake Catalogue of the Copiapó region December 2022 until June 2023
<p>Local Seismicity Catalogue of the Copiapó region (December 2022 until June 2023).</p> <p>The files contain the catalog, phase picks, and waveforms in SEISAN format. </p> <p>For details see Warwel et al. (submitted). </p> <p>This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) under grant 03G0297A (PISAGUA). </p>
San Diego Earthquake Dataset with Feature-Engineered Variables
<p>For the San Diego region, using data from the Southern California Earthquake Data Center (SCEDC), we filtered events by latitude 32.715, longitude -117.1611 within a 150 km radius, focusing on earthquake events from August 1, 2004, 00:00:00 to August 1, 2024, 00:00:00. All magnitude types and depths were included, and 21 variables were feature-engineered to enhance predictive modeling. This dataset provides a robust foundation for earthquake prediction in the San Diego area, incorporating both raw seismic data and advanced engineered features.</p>
Supplementary files to 'Along-strike extent of earthquakes on multi-segment reverse faults; insights from the Nevis-Cardrona Fault, Aotearoa New Zealand'
<p>Supplementary files to the article: Williams, J., Stirling, M., Langridge, R., Niroula, G., Vause, A., Stewart, J., Nicol, A., & Wang, N. (2024). Along-strike extent of earthquakes on multi-segment reverse faults; insights from the Nevis-Cardrona Fault, Aotearoa New Zealand. <em>Seismica</em>, <em>3</em>(2). https://doi.org/10.26443/seismica.v3i2.1310.</p> <p>Contents are:</p> <ul> <li>NCF_SuppInfo.pdf: Supplements S1-S4, where S1 are logs and photos from trenches excavated across the Nevis Fault in the 1980's, S2 are lidar-derived topographic profiles across scarps on the NW Cardrona segment, S3 is the laboratory report on the Stoney Creek and German Creek OSL samples, and S4 is the Upper Nevis Trench OxCal Model. Supplement S2 incorporates lidar provided by Toitū Te Whenua Land Information New Zealand (LINZ), and licensed under the Creative Commons Attribution 4.0 International licence (https://data.linz.govt.nz/layer/99123-otago-lidar-1m-dem-2016/).</li> <li>Unannotated orthomosiacs of the German Creek and Stoney Creek trench walls (as shown in Figures 5 and 6 in the manuscript)</li> <li>Digital surface models for the following Nevis segment localities: Stoney Creek, German Creek, Drummond Creek, and Coal Creek. The digital surface models (DSM) were derived from photos taken usig a DJI Phantom 3 Professional drone. The photos were then processed into a DSM using Agisoft structure for motion software. Ground control points were not used. Further details of the DSM generation are given in the manuscript.</li> </ul> <p>Any questions, please contact: jack.williams@otago.ac.nz</p>
Los Angeles, California, Earthquake Dataset with Feature-Engineered Variables
<p>This dataset includes detailed records of seismic events in Southern California, such as magnitudes, depths, and locations, filtered to focus on a 100 km radius around Los Angeles from January 1, 2012, to September 1, 2024. It also includes a target variable representing the maximum earthquake magnitude within 30 days of each event, along with additional engineered features for use in machine learning and neural network algorithms to improve earthquake forecasting.</p>
High-resolution DEMs and ortho images of Langtang village post-2015 Gorkha earthquake in Nepal
<p>Datasets related to the article "Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-seismic Avalanche in the Himalayas." The data were collected around Langtang village, which was destroyed by snow and ice avalanches triggered by the 2015 Gorkha earthquake. The datasets include digital elevation models (DEMs) and orthoimages, gathered using three types of UAVs equipped with different cameras in October 2015.<br><br>Description of files:<br>-a7_DEM_50cm.tif: 0.5 m resolution DEM derived from a quadcopter UAV equipped with a Sony α7R (36.3-megapixel sensor).<br>-a7_ortho_9cm.tif: 0.09 m resolution orthoimage derived from the same data as in a7_DEM_50cm.tif.<br>-ebee_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Canon IXUS 125HS (16-megapixel sensor).<br>-ebee_ortho_15cm.tif: 0.15 m resolution orthoimage derived from the same data as in ebee_DEM_50cm.tif.<br>-gr_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Ricoh GR (14.2-megapixel sensor).<br>-gr_ortho_12cm.tif: 0.12 m resolution orthoimage derived from the same data as in gr_DEM_50cm.tif.<br><br></p> <p>Please refer to the related journal article for more details on the datasets.</p> <p>Sunako S, Fujita K, Yamaguchi S, Inoue H, Immerzeel WW, Izumi T and Kayastha RB (2024) Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas. J. Disaster Res. 19(5), 865–873 (doi:10.20965/jdr.2024.p0865)</p>
Stress measurements for 42 Mw 4.0-5.4 earthquakes during the 2019 Ridgecrest earthquake sequence
<p>The csv file “Ridgecrest_table2_v1.csv” contains measurements of apparent stress, stress parameter, and corner frequency.</p> <p><strong>EvtID</strong>: Earthquake’s Event ID in Southern California Earthquake Data Center (SCEDC, https://scedc.caltech.edu).</p> <p><strong>Mw: </strong>Moment magnitude</p> <p><strong>AST: </strong>Apparent stress measurements in MPa using a time domain algorithm.</p> <p><strong>AST_STD: </strong>Standard deviation of AST in log10 units.</p> <p><strong>ASF: </strong>Apparent stress measurements in MPa using a frequency domain algorithm. Station terms have been corrected.</p> <p><strong>ASF_STD: </strong>Standard deviation of ASF in log10 units.</p> <p><strong>Brune: </strong>Stress parameter or Brune’s stress drop in MPa</p> <p><strong>Brune_STD: </strong>Standard deviation of ASF in log10 units. Note that the uncertainty is estimated using a bootstrap approach.</p> <p><strong>Fc: </strong>Corner frequency of the geometric mean source spectra</p> <p><strong>Fc_STD: </strong>Standard deviation of Fc in log10 units. Note that the uncertainty is estimated using a bootstrap approach.</p> <p><strong>ASF_sub: </strong>Apparent stress measurements in MPa using a frequency domain algorithm. Unlike ASF, only the stations with station terms less than 3 are used. No correction for station terms.</p> <p><strong>ASF_stack: </strong>Apparent stress measurements in MPa using the geometric mean source spectra.</p> <p><strong>Depth: </strong>Centroid depth in km</p> <p><strong>Vs: </strong>S wave velocity at the centroid depth in km/s</p> <p><strong>Density: </strong>Density at the centroid depth in Mg/m<sup>3</sup></p> <p> </p>
Earthquake Catalogs of the LArge-n Seismic Survey in Oklahoma dataset
<p>This dataset release contains earthquake catalogs created using different association methods for the LArge-n Seismic Survey in Oklahoma dataset. These datasets can be used by researchers to further analyze the earthquakes in the array to better understand their behavior. The association methods applied here are as follows the Guassian Mixture Model Association (GaMMA) (Zhu et al., 2022), PhaseLink (Ross et al., 2019), the Graph Earthquake Neural Interpretation Engine (GENIE) (McBrearty and Beroza, 2023) and Rapid Earthquake Association and Location (REAL) code (Zhang et al., 2019). For detailed information please see the paper that accompanies this dataset (Pennington et al. 2024). Important notes though, the GaMMA dataset has a large number of false events so it should be used with caution. The PhaseLink dataset does not associate S-phase arrivals so the catalog will only include P-wave arrivals. We also include in this dataset the original detected phase arrivals that each of these catalogs were created from to allow any user to test and apply new methods to and later compare to our results.</p>
Datasets and Codes for "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc"
<p>This Zenodo record contains the supplementary datasets and code for the paper titled "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc."<br><br></p> <p><strong>Datasets</strong></p> <ul> <li>phases.txt<br> Contains phases used for the event location and moment tensor inversion.<br> Format: ID, station, phase, year, month, day, secday<br> ID: Event identifier (same for all files)<br> station: Station name<br> phase: 1 for P-wave or 2 for S-wave<br> secday: Seconds in the day</li> <li>polarity.txt<br> Contains first motion P polarity used in the study.<br> Format: ID, station, polarity, trust, type<br> polarity: 1 for up or -1 for down<br> trust: Value between 0 and 1, indicating confidence level.<br> type: E for emergent or I for impulsive<br> Note that the arrival type has been automatically assigned and not double-checked.</li> <li>relocation.txt<br> HypoDD relocation file (see hypoDD manual for full description).<br> Format: ID, LAT, LON, DEPTH, X, Y, Z, EX, EY, EZ, YR, MO, DY, HR, MI, SC, MAG, NCCP, NCCS, NCTP, NCTS, RCC, RCT, CID</li> <li>MT_soluton.txt<br> Contains all double-couple moment tensor solutions.<br> Format: ID, strike, dip, rake, mag, kagan_std<br> mag: Moment magnitude (Mw); "None" if the event is not considered stable<br> kagan_std: Quality interpretation of the moment tensors using Kagan angle standard deviation, as described in the main paper.</li> </ul> <p> </p> <p><strong>Codes</strong></p> <p>Future development of the relative moment tensor algorithm will be conducted on GitHub as part of the Marie-Sklodowska-Curie Action relMT funded by the European Union (https://github.com/wasjabloch/relMT)</p> <p>Here are the files in Codes.zip:</p> <ul> <li>synthetics.zip<br> Contains codes for performing and testing synthetic moment tensor inversion.</li> <li>relMT.zip<br> Contains the code for performing moment tensor inversion on real data.</li> <li>intrustion.txt<br> Contains instructions for setting up and running the codes.</li> <li>environment_MAC.yml<br> File to create the python environment on a MAC or LINUX machine.</li> <li>environment_WINDOWS.yml<br> File to create the python environment on a WINDOWS machine.</li> </ul>
Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards
<p>Raw data of "Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards"</p>
Data for: A further source of Tokyo earthquakes and Pacific Ocean tsunamis
<p>This data repository contains the location of cores collected as part of this study, the microfossil and radiocarbon results from those cores, and fault parameters used in eleven historical and hypothetical tsunami simulations for Kujukuri, Japan (Boso Peninsula).</p> <p>The work is supported by the Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST) and in part by grants awarded to J.E.P. [National Science Foundation (EAR-1303881 and 1624612), Natural Sciences and Engineering Council of Canada (NSERC), Canada Research Chair (CRC) program, and Japan Society for the Promotion of Science (JSPS) International Research Fellow program at the Geological Survey of Japan (PE14038)]; A.C.P. [Science Foundation Ireland Career Development Award (17/CDA/4695), Investigator Award (16/IA/4520), Marine Research Programme funded by the Irish Government, co-financed by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01), European Union’s Horizon 2020 research and innovation programme under grant agreement No 818144, and SFI Research Centre (16/RC/3872 and 12/RC/2289_P2); and B.P.H. [Singapore Ministry of Education Academic Research Fund (MOE2019-T3-1-004), National Research Foundation Singapore, and Singapore Ministry of Education, under the Reseach Centers of Excellence initiative].</p>
Moment rate functios of shallow very low frequency earthquakes southeast off the Kii Peninsula, along the Nankai Trough, Japan
<p>Moment rate functions of shallow very low frequency earthquakes (VLFEs) that occurred southeast off the Kii Peninsula, along the Nankai Trough</p> <p>The method and related discussions were described in the <a href="https://doi.org/10.1029/2021JB023073">JGR paper</a>.</p> <p><strong>Included files</strong></p> <ul> <li>YYYY-MM-DDThhmmssparam.stf<br> Parameter file for the Monte-Carlo-based simulated annealing estimation for a shallow VLFE occurred at hh:mm:ss on DDth MM YYYY (JST). Detection time, correlation coefficient, longitude, latitude, ratio (internal parameter), template index (internal parameter), assumed strike angle, dip angle, rake angle, source grid index (internal parameter), the number of the used stations, station list are included.</li> <li>YYYY-MM-DDThhmmss_STF.dat<br> Moment rate function for a shallow VLFE occurred at hh:mm:ss on DDth MM YYYY (JST). The optimal and original simulated annealing estimations are listed in the 2nd and 3rd columns, respectively. The time from the origin is represented in the 1st column</li> <li>VLFE_catalog.csv<br> CSV format file of Shallow VLFE catalog from Apr. 2004 to Mar. 2021. Origin time (JST), origin time (UTC), longitude (ºE), latitude (ºN), seismic moment (Nm), duration (s), VR (%), and Mw are listed.</li> </ul> <p><strong>Citation</strong></p> <ul> <li>Takemura, S., Obara, K., Shiomi, K., Baba, S. (2021), Spatiotemporal variations of shallow very low frequency earthquake activity southeast off the Kii Peninsula, along the Nankai Trough, Japan <a href="https://doi.org/10.1029/2021JB023073">https://doi.org/10.1029/2021JB023073 </a></li> <li>This data doi</li> </ul> <p> </p>
Mechanical dataset for nanometric flow and earthquake instability
<p>Mechanical data of rock deformation experiments in:</p> <p>Nanometric flow and earthquake instability</p> <p>MATLAB codes are also provided to reproduce related figures in the paper.</p> <p>---------------------------------------------------------------------------<br> By Hongyu Sun and Matej Pec</p> <p>Department of Earth, Atmospheric and Planetary Sciences<br> Massachusetts Institute of Technology</p> <p>* Corresponding Authors: hongyus@mit.edu, mpec@mit.edu</p>
Local Earthquake Tomography Code and Data for study the Lithosphere Structure in the Collision Zone of the NW Himalayas
<p>The tomography model presented in the paper "Lithosphere Structure in the Collision Zone of the NW Himalayas Revealed by Local Earthquake Tomography" are obtained using the LOTOS code by Koulakov (2009). Here, we present the full version of the code with initial data and parameters used for calculating P and S velocity models beneath the NW Himalaya. This version of the code is adopted for the Windows OS and contains the entire program listing and the full project structure for Microsoft Visual Studio 2010 and Intel Visual Fortran. Detailed description of the code can be found at <a href="http://www.ivan-art.com/science/LOTOS">www.ivan-art.com/science/LOTOS</a></p>
Vrancea Earthquakes 3D Scatterplot
<p>Scatterplot of earthquakes with magnitudes larger than 1 from Vrancea, Romania seismic region. Here are presented as scatterplot points 7512 earthquakes, with the size and colour of the points indicating their magnitude, starting from from 1976-08-19 19:03:01 to 2021-02-28 00:11:55.</p>
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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)
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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.