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781 results for “earthquakes”
Mechanism of the 2017 Mw 6.3 Pasni earthquake and its significance for future major earthquakes in the eastern Makran
<p>On 7th February 2017, a moment magnitude (M<sub>w</sub>) 6.3 earthquake rattled offshore Pasni in the eastern Makran and triggered a small tsunami. Using a combination of seismicity, multibeam bathymetry, seismic profile, InSAR measurements, and tide gauge observation, we conduct an in-depth investigation into the seismogenic structure, coseismic deformation, and tsunami characteristics of this event. Our results indicate that (1) the earthquake occurred on the shallow-dipping (3-4°) megathrust; (2) the megathrust co-seismically slipped 15 cm and caused ~2-4 cm ground subsidence and uplift at Pasni; (3) our tsunami modeling reproduces the observed 5-cm-high small tsunami waveforms. The Pasni earthquake rupture partially overlaps the 1851 and 1945 earthquake (M>8) slip patches, releasing estimated 3% and 7% of accumulative strain since then. With such stress perturbation, the Pasni earthquake could promote failure of megathrust in the future. This study calls for more preparedness in mitigating earthquake and associated hazards in the eastern Makran. </p>
Inventory of landslides triggered by the 2015 Mw 6.0 Sabah earthquake (Malaysia)
<p>These files are related to the paper “Landslides triggered by the 2015 Mw 6.0 Sabah (Malaysia) earthquake: inventory and ESI-07 intensity assignment” by Ferrario M.F., submitted to NHESS</p> <ul> <li>Shapefile of the mapped landslides and study area</li> <li>Spreadsheet with data on inventories of earthquake-triggered landslides</li> </ul>
Source models for "Across-slab propagation and low stress drops of deep earthquakes in the Kuril subduction zone"
<p>This repository is for the model results for eight deep earthquakes in the Kuril subduction zone modelled using a second-degree moments method in csv format.</p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Event is the GCMT event code. Aspect ratio is the ratio (Amin/Amax). Duration is the rupture duration; Amax is the maximum characteristic fault dimension; Amin is the minimum characteristic fault dimension; Phi is the angle between Amax and the strike; v0 is the centroid velocity; Theta is the angle between the centroid velocity and the strike; and mft is the misfit between the data and the higher-order synthetics calculated for the best-fitting source model obtained from the Monte Carlo inversions.</p> <p> </p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subhorizontal.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Column headers are the same as in <a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a>.</p>
Stanford fiber-optic DAS array: Earthquake detection dataset
<p>Repurposing the fiber-optic cables from the existing telecommunication infrastructure makes it possible to record dense continuous seismic data in urban areas at low cost. From 2016 to 2019, we connected a disctributed acoustic sensing (DAS) interrogator unit to the fiber-optic cables in telecommunication conduits under Stanford University campus, recording years of continuous seismic data.</p> <p>This repository contains processed TensorFlow Record data files containing examples of earthquake and background noise signals recorded by the Stanford fiber-optic DAS array. These data were used for training, evaluation, and testing of a convolutional neural network for earthquake detection. </p> <p> </p> <p> </p>
Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling."
<p>Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling." </p>
Earthquake relocated catalog - Western Corinth Gulf - 2020-12-20 2021-02-28
<p>Relocated catalog in growclust format (Trugman & Shearer, 2017). The content of this material is described in Zahradník et al. 2022.</p> <p> </p> <p>References :</p> <p>Trugman, D. T., & Shearer, P. M. (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. <em>Seismological Research Letters</em>, <em>88</em>(2A), 379-391.</p>
Earthquakes worldwide alert
<p>Dataset with data from Earthquake Hazard Program with a subset of the latest earthquakes registered since 1984 with a Magnitude of 2.5+</p>
Lithospheric deformation due to the 2015 M7.2 Sarez (Pamir) earthquake constrained by 5 years of space geodetic observations
<p>This zip file contains observations of postseismic surface deformation due to the 2015 Mw 7.2 Sarez earthquake derived from Sentinel-1 and ALOS-2 SAR data and GNSS positions over a time period of five years after the mainshock.</p>
Environmental effects of the December 2020 earthquake sequence near Petrinja, Croatia
<p>Those files (Spreadsheet of observations of coseismic deformation features, Surface rupture shapefile) are in support of the paper submitted to Geophysical Journal International. The data have been collected by the coauthors of this paper, implemented in the files by them, and the database has been processed and verified by SB, AT, SP.</p> <p>The complete of authors of the paper is as follows:</p> <p>S. Baize<sup>1</sup>, S. Amoroso<sup>2,3</sup>, N. Belić<sup>4</sup>, L. Benedetti<sup>5</sup>, P. Boncio<sup>2</sup>, M. Budić<sup>4</sup>, F.R. Cinti<sup>3</sup>, M. Henriquet<sup>5</sup>, P. Jamšek Rupnik<sup>6</sup>, B. Kordić<sup>4</sup>, S. Markušić<sup>7</sup>, L. Minarelli<sup>3</sup>, D. Pantosti<sup>3</sup>, S. Pucci<sup>3</sup>, M. Špelić<sup>4</sup>, A. Testa<sup>2</sup>, S. Valkaniotis<sup>8</sup>, M. Vukovski<sup>4</sup>, J. Atanackov<sup>6</sup>, J. Barbača<sup>4</sup>, M. Bavec<sup>6</sup>, R. Brajkovič<sup>6</sup>, V. Brčić<sup>4</sup>, M. Caciagli<sup>3</sup>, B. Celarc<sup>6</sup>, R. Civico<sup>3</sup>, P.M. De Martini<sup>3</sup>, R. Filjak<sup>4</sup>, F. Iezzi<sup>2</sup>, A. Moulin<sup>5</sup>, T. Kurečić<sup>4</sup>, M. Métois<sup>9</sup>, R. Nappi<sup>3</sup>, A. Novak<sup>6,10</sup>, M. Novak<sup>6</sup>, B. Pace<sup>2</sup>, D. Palenik<sup>4</sup>, T. Ricci<sup>3</sup></p> <p><sup>1</sup> Institut de Radioprotection et de Sûreté Nucléaire, IRSN/PRP-ENV/SCAN/BERSSIN, 92262 Fontenay-Aux-Roses, France</p> <p><sup>2</sup> Università “G. d'Annunzio” Chieti - Pescara, Via dei Vestini 31, 66100, Chieti, Italy</p> <p><sup>3</sup> Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy</p> <p><sup>4</sup> Croatian Geological Survey, Department of Geology, Sachsova 2, Zagreb, 10000, Croatia</p> <p><sup>5</sup> Aix Marseille Univ, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France</p> <p><sup>6</sup> Geological Survey of Slovenia, GeoZS, Dimičeva ulica 14, 1000 Ljubljana, Slovenia</p> <p><sup>7</sup> University of Zagreb, Faculty of Science, Department of Geophysics, Zagreb, Croatia</p> <p><sup>8</sup> Department of Civil Engineering, Polytechnic School, Democritus University of Thrace, 67100 Xanthi, Greece</p> <p><sup>9 </sup>Laboratoire de Géologie de Lyon (LGLTPE), Université Claude Bernard Lyon 1, campus de La Doua, 69100 Villeurbane, France</p> <p><sup>10 </sup>University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Geology, Croatia</p>
Structure controlled earthquake behaviors induced by shale gas development in southern Sichuan basin, China
<p>This is the dataset available for the article titled "Structure controlled earthquake behaviors induced by shale gas development in southern Sichuan basin, China", including earthquake catalog, waveforms of 53 moderate earthquakes and three dimentional shear wave velocity model.</p>
Geodetic observations for the 2016 Mw5.9 and 2022 Mw6.7 Menyuan earthquakes
<p>Geodetic observations for the 2016 Mw5.9 and 2022 Mw6.7 Menyuan earthquakes, including the InSAR observations from interferograms and pixel offsets from SAR images</p>
On the trail of fluids in the northernmost intracontinental earthquake swarm areas of the Leipzig-Regensburg fault zone, Germany
<p>rf.dat contains a migrated receiver functions profile (X, Z, Amplitude) between 50°N and 51.15°N.</p> <p>simul_vel.dat contains a 3D velocity model obtained with SIMULPS14 (Longitude, Latitude, Depth, Vp, Vs).</p> <p>velest_vel.dat contains a 1D velocity model obtained with VELEST (Vp or Vs, Depth, Weighting).</p>
An improved earthquake catalog during the 2018 Kilauea eruption from combined onshore and offshore seismic arrays
<p>The Island of Hawai'i was formed by repeated eruptions of basalts at an oceanic hotspot. Kilauea, the youngest among the subaerial volcanoes of the island, erupted intensely in 2018. The eruption provided an opportunity to look into the mechanisms that operate at the volcano and associated earthquake activities, as it was recorded simultaneously, for the first time, by onshore and offshore seismometers. We used most of the publicly available seismic data during the eruption period, including temporary arrays, to build a more complete earthquake catalog during the eruption than that provided by the Hawaiian Volcano Observatory (HVO). We used a short-time-average/long-time-average (STA/LTA) method to identify potential earthquakes. The detections were associated into events and automatically picked with P- and S-wave arrivals, which were used to locate the events in a three-dimensional velocity model. After re-examining these earthquake events, their coda/duration magnitudes were determined. The resulting half-year catalog contains 375,736 events with one of the highest daily earthquake numbers ever reported (6,128 on June 21st, 2018). A great number of events were recorded during the caldera collapses, from its beginning untill its rapid ending. The catalog also contains abundant events near the Pu'u'o'o vent and in the lower East Rift Zone, where an increase of seismicity in the mid-July and August indicated a step-up in magma intrusion after the eruption.</p>
High Rate GNSS Velocities for Earthquake Strong Motion Signals
<p>This zipped dataset consists of directories by earthquake of 5Hz GNSS velocity time series.</p> <p>── comcat_earthquake_code</p> <p>└── velocities_4char_DOY_YYYY.txt</p> <p> </p> <p>References:</p> <ul> <li><a href="https://earthquake.usgs.gov/data/comcat/">USGS Comcat Catalog</a> </li> <li><a href="https://www.unavco.org/data/gps-gnss/gps-gnss.html">UNAVCO Archive 4char station codes</a></li> <li><a href="http:// https://github.com/crowellbw/SNIVEL">SNIVEL processing software repo</a></li> </ul>
Data and modeling input and parameter files for the 2021 Acapulco, Mexico earthquake and tsunami
<p>Raw and processed strong motion, GNSS, InSAR and tide gauge data for the event. Also includes MudPy slip inversion parameter files as well as GeoClaw input files.</p>
Dataset for a tutorial to quantify BAM earthquake using SNAP
<p><strong>A tutorial to quantify BAM earthquake using SNAP</strong></p> <p>Differential InSAR is a satellite-based remote sensing technique that can be used to quantify small displacements of the Earth's surface. This is due to the interferometric phase being much more sensitive to the ground motion than to the elevation difference. This practical session will explain how to apply it to real-world Envisat ASAR images, with user-oriented open-source SNAP software. The main goal is able to generate ground motion from a pair of SAR images to map the Earthquake of 2003 in BAM city.</p> <p>More information can be found here: </p> <p>Video: https://youtu.be/Uc-5F9Vz04w</p> <p>https://github.com/BAMInSAR</p> <p>https://www.facebook.com/groups/RadarInterferometry</p>
InaTEWS Earthquake Station Dataset
<p>Spectrogram Dataset for classification Earthquake Station Quality (InaTEWS Earthquake Station).</p>
Data from: Recovery of macrobenthic communities in tidal flats following the Great East Japan Earthquake
<p><span>Extreme geo-climatic events can occur everywhere in the world. The Great East Japan Earthquake, with a moment magnitude scale of 9.0 M on March 11, 2011, was one of these events and caused huge tsunamis that changed largely tidal communities on the Pacific coast of eastern Japan. However, subsequent long-term changes in the communities after such an extreme geo-climatic event have rarely been examined. Therefore, we performed the biological monitoring over a ten-year span, before and after the earthquake, that was supported by citizen volunteers. The results revealed that the most tidal communities returned to their original states in less than a decade, indicating that the nearshore communities were highly resilient to large physical disturbances like tsunamis if the surrounding environmental conditions were unchanged.</span></p>
Supporting Information for "Quantifying the statistical relationships between flank eruptions and major earthquakes at Mt. Etna volcano (Italy)". Data Sets S1-S7.
<p>This compressed folder contains supporting information related to the manuscript: "Quantifying the statistical relationships between flank eruptions and major earthquakes at Mt. Etna volcano (Italy)".</p> <p>Data Set S1. Catalog of flank eruptions<br> Historical catalog of flank eruptions of Mt. Etna from 1600 to 2018.</p> <p>Data Set S2. Catalog of major earthquakes<br> Macroseismic catalog of Etnean earthquakes from 1800 to 2018.</p> <p>Data Set S3. GIS dataset of Eruptive fissures<br> GIS shapefiles of eruptive fissures at Mt. Etna from 1800 to 2018. UTM WGS84, Zone 33 N.</p> <p>Data Set S4. Tests with ±2 months maximum inter-event time <br> Histograms of the inter-event time of earthquakes and flank eruptions lesser than ±2 months. Pie charts of the positive values (dark colors), negative values in [-2.5, 0] days (light colors), and lower than -2.5 days (white) are reported.</p> <p>Data Set S5. Tests with dt = 5 days moving window <br> Conditional rates of major earthquakes less than ±4 months from flank eruptions, obtained assuming dt = 5 days instead of dt = 10 days. A solid line marks the average annual rate of the earthquakes, and bold lines threshold rates 2, 5, and 10 times larger than the average value.</p> <p>Data Set S6. Tests on the eruptions end, including earthquakes in ±2.5 days from the onset<br> Conditional rates of major earthquakes less than ±4 months from flank eruptions end, obtained without excluding the earthquakes occurred in ±2.5 days from the onset. A solid line marks the average annual rate of the earthquakes, and bold lines threshold rates 2, 5, and 10 times larger than the average value.</p> <p>Data Set S7. Summary of inter-event time histograms <br> Histograms of the inter-event time of earthquakes and flank eruptions lesser than ±4 months, also decomposed according to spatial groups E1-E4 and fault systems F1-F4. Light-colored bars highlight the eruptions > 2850 m.a.s.l.</p>
Two multi-temporal datasets to track debris flow after the 2008 Wenchuan earthquake
<p>We provide two datasets for tracking the debris flow induced by the 2008 Wenchuan Mw 7.9 earthquake on a section of the Longmen mountains on the eastern side of the Tibetan plateau (Sichuan, China). The database was obtained through a literature review and field survey reports in the epicenter area, combined with high-resolution remote sensing imagery and extensive data collection and processing. The first dataset covers an area of 892 km<sup>2</sup>, including debris flows from 2008 to 2020. 186 debris flows affecting 79 watersheds were identified. 89 rainfall stations were collected to determine the rainfall events for the post-earthquake debris flow outbreak. The second database is a list of mitigation measures for post-earthquake debris flows, including catchment name, check dam number, coordinates, construction time, and successful debris flow mitigation date. This two datasets can aid different applications, including the early warning monitoring and engineering prevention of post-earthquake debris flow, as well as provide valuable data support for research in related disciplines.</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.