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781 results for “earthquakes”
Potential short-term earthquake forecasting by farm animal monitoring
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Fault asperities and the transition from aseismic creep to stick-slip: implications for earthquake precursors
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Cascading impacts of earthquakes and extreme heatwaves have destroyed populations of an iconic marine foundation species
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Dataset for: Impacts of a Cascadia subduction zone earthquake on water levels and wetlands of the lower Columbia river and estuary
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Iquique earthquake preparatory phase / Aden-Antoniow 2020
<p>Seismic catalog created for the study "Statistical evidence of a seismic quiescence before the Mw8.1 Iquique earthquake, Chile"</p> <p> </p>
LEN-DB - Local earthquakes detection: a benchmark dataset of 3-component seismograms built on a global scale
<p>In this study ( <a href="http://www.sciencedirect.com/science/article/pii/S2666544120300010">The paper</a> ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: <em>EQ</em> and <em>AN</em>. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format <em>net_sta_starttime</em>, where <em>net</em>, <em>sta</em>, and <em>starttime</em> represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group <em>Stations</em> allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format <em>net_sta)</em> as the number of receivers employed for collecting the waveforms.</p> <p>This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.</p>
Shear wave splitting measurements used in "Spatio-temporal Analysis of Seismic Anisotropy Associated with the Cook Strait and Kaikoura Earthquake Sequences in New Zealand"
<p>Shear wave splitting measurement used in "Spatio-temporal Analysis of Seismic Anisotropy Associated with the Cook Strait and Kaikoura Earthquake Sequences in New Zealand" is included here. This CSV and XLSX file are part of a paper submitted to GJI in April 2020. A detailed description of the column headers can be found in the MFAST manual, in table 4, at http://mfast-package.geo.vuw.ac.nz/mfast_manual_v2.2.pdf</p>
Evidence for fluids at the hypocenter of the 2017 Ms 7.0 Jiuzhaigou earthquake revealed by local earthquake tomography
<p>This dataset presented here is used in the manuscript entitled "Evidence for fluids at the hypocenter of the 2017 Ms 7.0 Jiuzhaigou earthquake revealed by local earthquake tomography".</p>
Terrestrial Laser Scans of surface deformation associated with the 11/11/2019 Mw 4.7 Le Teil earthquake (SE France)
<p>Terrestrial Laser Scans of surface deformation produced by the 11/11/2019 Mw 4.7 Le Teil earthquake in SE France.</p> <p>All scans were produced with a Faro X330 equipment at 1/2 resolution, low laser power, with in-field filters (lost points). Processing includes import and registration with Faro Scene software, export as LAS files, manual editing of noise, vegetation and scattered points with CloudCompare software and rasterization with universal kriging with Golden Software Surfer software.</p>
Digitized waveforms of the 1952 Tokachi-oki earthquake
<p>Digitized waveforms of the 1952 Tokachi-oki earthquake used in Kobayashi et al. Titled: Similarities and differences in the rupture processes of the 1952 and 2003 Tokachi-oki earthquakes. Journal of Geophysical Research: Solid Earth. submitted.</p>
LOTOS coce of local earthquake tomography with the Akutan dataset
<p>This depositary contains the data and the program codes that can be used to reproduce all the results presented in the article:</p> <p>I. Koulakov, V. Komzeleva, S.Z. Smirnov and S.B. Bortnikova (2020), Magma-fluid interactions beneath the Akutan Volcano in Aleutian Arc based on the results of local earthquake tomography, <em>Journal of Geophysical Research, Solid Earth</em> (submitted)</p> <p>The tomography models presented in the paper 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 Akutan volcanic Island in the Aleutian arc (USA). 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> <p>Koulakov, I. (2009). LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms. <em>Bulletin of the Seismological Society of America</em>, 99(1), 194–214. https://doi.org/10.1785/0120080013</p>
Three-dimensional numerical simulation of the interseismic and coseismic phases associated with the 6 April 2009, Mw 6.3 L'Aquila earthquake (Central Italy)
<p>Results of the numerical model expressed in terms of nodal stresses, strains and displacements at the end of the interseismic and coseismic phases.</p>
Can Precursory Moment Release Scale With Earthquake Magnitude? A View From the Laboratory
<p>This is the data repository for '</p> <p>Can Precursory Moment Release Scale With Earthquake Magnitude? A View From the Laboratory'</p> <p> </p> <p>This is the ReadMe file corresponding to the study entitled: "Can Precursory moment release scale with earthquake magnitude? A view from the laboratory."<br> By M. Acosta, F. X. Passelègue, A. Schubnel, R.Madariaga & M. Violay.<br> This study has been published in the Journal Geophysical Research Letters in November 2019. https://doi.org/10.1029/2019GL084744<br> This Read-Me file has been last edited on 2019-12-11<br> This readme file describes the data repository and supplementary files accompanying the above publication. For any further queries please contact mateo.acosta@epfl.ch<br> The following files are included:<br> --- Regarding Figure 1.<br> 1) "Acosta_et_al_2019_Figure1Data.xlsx" This is the processed data from the experiments described in Figure1 of the article.In this .xlsx File, each sheet corresponds to one figure panel as follows: <br> Fig.1a: Shear stress vs Slip Dry ExperimentColumn A: Dry experiment slip in (mm) ; Column B: Dry experiment near fault shear stress in (MPa);<br> Fig.1b: Shear stress, Slip, and AE's Vs time to mainshock Dry ExperimentColumn A: Dry experiment time in (s); Column B: Dry experiment slip in (mm) ; Column C: Dry experiment near fault shear stress in (MPa); Column D: Insert time (for Acoustic emissions) ; Column E: Acoustic emissions.<br> Fig.1c: Fault coupling Vs time to mainshock Dry experimentColumn A: time to mainshock; Columns B-K: Fault coupling for events 1-10 respectively<br> Fig.1d: Shear stress vs Slip Pf=1MPa ExperimentColumn A: Pf=1MPa experiment slip in (mm) ; Column B: Pf=1MPa experiment near fault shear stress in (MPa);<br> Fig.1e: Shear stress, Slip, and AE's Vs time to mainshock Pf=1MPa ExperimentColumn A: Pf=1MPa experiment time in (s); Column B: Pf=1MPa experiment slip in (mm) ; Column C: Pf=1MPa experiment near fault shear stress in (MPa); Column D: Insert time (for Acoustic emissions) ; Column E: Acoustic emissions.<br> Fig.1f: Fault coupling Vs time to mainshock Pf=1MPa experiment.Column A: time to mainshock; Columns B-F: Fault coupling for events 1-5 respectively.<br> <br> <br> --- Regarding Figures 2 and 3 ""Acosta_et_al_2019_Figure2and3Data.xlsx"""The data file contains an extended data table that allows to reproduce all the Figures in the article. All the other information is contained in the article or supplementary material.</p>
Tsunami Efficiency due to Very Slow Earthquakes
<p>Figures for Random hypocenter of stochastic scenarios</p>
Waveform data of earthquakes in Korea
<p>Waveform data on earthquakes in "ASCII" format</p>
Supporting Information for "How erosion influence fault segmentation and earthquakes in thrust belts: Low-temperature thermochronology and fluvial shear stress analyses on the southern Longmen Shan, eastern Tibet"
<p>Supporting Information for</p> <p>How erosion influence fault segmentation and earthquakes in thrust belts: Low-temperature thermochronology and fluvial shear stress analyses on the southern Longmen Shan, eastern Tibet</p> <p>Yijia Ye<sup>1</sup>, Xibin Tan<sup>1, </sup>*, Yiduo Liu<sup>2</sup>, Feng Shi<sup>1</sup>, Yuan-Hsi Lee<sup>3</sup>, Michael A. Murphy<sup>2</sup>, Xiwei Xu<sup>4</sup></p> <ol> <li>State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, 100029, China</li> <li>Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77204-5007, USA</li> <li>Department of Earth and Environmental Sciences, National Chung-Cheng University, Chia-Yi, 62102, Taiwan</li> <li>Institute of Crustal Dynamics, China Earthquake Administration, Beijing, 100085, China</li> </ol> <p><em>* </em>Corresponding author. E-mail address: <a href="mailto:tanxibin@sina.com">tanxibin@sina.com</a></p> <p> </p> <p><strong>Contents of this file </strong></p> <p>Figures S1 and Table S1</p>
Data used in the figures of "Aseismic deformation during the 2014 Mw 5.2 Karonga earthquake, Malawi from InSAR and earthquake source mechanisms"
<p>Data used in Figures 2, 4, and SI3 of the paper "Aseismic deformation during the 2014 Mw 5.2 Karonga earthquake, Malawi from InSAR and earthquake source mechanisms."</p>
Earthquakes in Spain - August to October 2020
<p>Earthquakes in Spain from 1st August 2020 to 30th October 2020.</p> <p>Fields:</p> <ul> <li>Date (DD/MM/YYYY)</li> <li>Time UTC (hh:mm:ss)</li> <li>Latitude (signed float)</li> <li>Latitude Cardinal (character)</li> <li>Longitude (signed float)</li> <li>Longitude Cardinal (character)</li> <li>Depth (integer)</li> <li>Magnitude Type (string)</li> <li>Magnitude (unsigned float)</li> <li>Region (string)</li> <li>Last Update (DD/MM/YYYY hh:mm:ss)</li> <li>Earthquake ID (integer)</li> </ul>
Earthquake catalogue for Tarawera region, March 2019
<p>Earthquake catalogue in QuakeML format for the Tarawera region, New Zealand, March 2019. If you use this dataset please cite the following publication:</p> <p>Benson, T.W., Illsley-Kemp, F., Elms, H.C., Hamling, I.J., Savage, M.K., Wilson, C.J.N., Mestel, E.R.H., Barker, S.J. Earthquake Analysis suggests dyke intrusion in 2019 near Tarawera volcano, New Zealand. <em>Frontiers in Earth Sciences</em>, 2021, DOI=10.3389/feart.2020.606992.</p> <p> </p>
Earthquakes
<pre>This dataset contains information on earthquakes produced during the year 2020 (more exactly until November). The information it contains is date, time, magnitude, geographic coordinates, place and country. The data is the result of web scraping on the Volcano Discovery page.</pre> <p>Source: volcanodiscovery.com</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.