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662 results for “seismicity”
Dataset for "Investigating multiple observations around a seismic gap during the Lushan earthquake in Sichuan, China"
<p>"MsCatalog.csv" includes the catalog used in the paper.</p> <p>"gnss2013" includes the daily horizontal surface displacement (E and N components) in the South China Block frame in 2013.</p> <p>“2013.tar.gz” includes the Empirical Green's functions (EGF) of all 208 station pairs in 2013, Sichuan, which were derived from the continuous seismic waveforms (ambient noise). </p>
Seismic evidence for a weakened thick crust at the Beaufort Sea continental margin
<p>This compressed folder contains raw seismic waveforms used to determine the three-dimensional seismic velocity structure beneath the Beaufort Sea continental margin of northwestern Canada.</p> <p>Estève, C.,<strong> </strong>Liu, Y., Koulakov, I., Schaeffer, A.J, and Audet, P., Seismic evidence for a weakened thick crust at the Beaufort Sea continental margin <em>(Geophysical Research Letters</em>).</p>
Data for "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" (Subset 3)
<p>This is dataset of ocean wave simulations used in the paper "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" by Shimura et al. (submitted).</p> <p>The dataset contains </p> <ul> <li>significant wave height (Subset 1),</li> <li>wave induced surface pressure (Subset 2)</li> <li>long-period componet of wave heights (Subset 3)</li> <li>long-period component of surface pressure (Subset 4)</li> </ul> <p>during 2004 from 2023 summer. </p>
High-resolution seismic record of the Quaternary palaeoenvironments along a Dalmatian-type coast (Lošinj Channel, Adriatic Sea)
<p>Raw seismic files used in the manuscript High-resolution seismic record of the Quaternary palaeoenvironments along a Dalmatian-type coast (Lošinj Channel, Adriatic Sea) published in journal Marine Geology.</p>
Noise samples for Costantino et al., Seismic Source Characterization From GNSS Data Using Deep Learning (2023)
Open the record for dataset details and reuse information.
Seismic scattering regimes from multiscale entropy and frequency correlations dataset
<p>Dataset accompanying manuscript published in Geophysical Journal International entitled</p> <p><a href="doi.org/10.1093/gji/ggae098"><em>Seismic scattering regimes from multiscale entropy and frequency correlations</em></a></p> <p> </p>
Seismic crustal structure of the North China Craton and surrounding area: Synthesis and analysis
<p>Nccrust is a new digital model (NCcrust) of the seismic crustal structure of the Neoarchean North China Craton (NCC) and its surrounding Paleozoic-Mesozoic orogenic belts (30–45∘N, 100–130∘E).</p> <p>All available seismic profiles, complemented by receiver function interpretations of crustal thickness, are used to constrain a new comprehensive crustal model NCcrust.</p> <p>The model, presented on a 0.25∘ × 0.25∘grid, includes the Moho depth and the internal structure (thickness and velocity) of the crust specified for four layers (the sedimentary cover, upper, middle, and lower crust) and the Pn velocity in the uppermost mantle.</p> <p>Xia, B., Thybo, H. & Artemieva, I. M. Seismic crustal structure of the North China Craton and surrounding area: Synthesis and analysis. J. Geophys. Res.-Solid Earth 122, 5181-5207, doi:10.1002/2016jb013848 (2017).</p> <p>Any questions or requests, please has no hesitation in email me: bingxia0127@gmail.com</p>
Seismic Data From Arroyo de los Pinos experiment
<p>Seismic dataset collected at Arroyo de los Pinos channel, containing vertical component broadband seismic channel for specific time periods referenced in Luong et al., (submitted 2024) and McLaughlin et al. (submitted, 2024).</p>
Aseismic slip and seismic swarms leading up to the 2024 M7.3 Hualien earthquake
<p>This repository contains the dataset and slip models in Peng et al. (2025), "Aseismic slip and seismic swarms leading up to the 2024 M7.3 Hualien earthquake".</p>
Relation between seismic noise levels and soil fauna
<p><span><span><span><span><span><span><span><span><span><span><span>Human activities often impact the sensory environment of organisms. Wind energy turbines are a fast-growing potential source of anthropogenic vibrational noise that can affect soil animals sensitive to vibrations and thereby alter soil community functioning. Larger soil animals, such as earthworms (macrofauna, > 1 cm in size), are particularly likely to be impacted by the low-frequency turbine waves that can travel through soils over large distances. Here we examine the effect of wind turbine-induced vibrational noise on the abundance of soil animals. We measured vibrational noise generated by seven different turbines located in organically-farmed crop fields in the Netherlands. Vibratory noise levels dropped by an average of 23 ± 7 dB over a distance of 200 m away from the wind turbines. Earthworm abundance showed a strong decrease with increasing vibratory noise. When comparing the nearest sampling points in proximity of the wind energy turbines with the points furthest away, abundance dropped on average by 40% across all seven fields. The abundance of small-sized soil animals (mesofauna, < 10 mm in size) differed between crop fields, but was not related to local noise levels. Our results suggest that anthropogenic vibratory noise levels can impact larger soil fauna, which has important consequences for soil functioning. Earthworms, for instance, are considered to be crucial ecosystem engineers and an impact on their abundance, survival and reproduction may have knock-on effects on important processes such as water filtration, nutrient cycling and carbon sequestration.</span></span></span></span></span></span></span></span></span></span></span></p>
Dataset - Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator
<p>The directory contains velocity models from OpenFWI dataset collection. These velocity models are the inputs of the FNO-based sovlers in the paper <strong><em>Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator</em></strong>.</p>
Seismic attenuation extraction from traffic signals recorded by a single seismic station (3/3)
<p>This repository contains the raw field data and corresponding codes for the West Coast Park site in the manuscript entitled "Seismic attenuation extraction from traffic signals recorded by a single seismic station " by Yumin Zhao, Enhedelihai Nilot, Bei Li, Yunyue Elita Li, and Gang Fang. The data is about 7-month long. This repository contains the last two months of the data. The first 5 months of the data can be found at https://zenodo.org/record/4905734#.Y3MaoXZByUk.</p>
Dataset and 3D Vs Model for "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography"
<p>Final 3D Vs model and dispersion data for the paper entitled "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography".</p> <p>In the vel_files folder, there are 10 files for each depth for 1-15 km. The format of each velocity file is as follows:</p> <p>Column Value<br> 1 Lattitude (°)<br> 2 Longitude (°)<br> 3 Vs (km/s)</p> <p>The format of the dispersion data is as follows (See <a href="https://www.eas.slu.edu/eqc/eqc_cps/TUTORIAL/EMPIRICAL_GREEN/example1.html">Computer Programs in Seismology Tutorials - do_mft</a> for more information on the format):</p> <p>Column Value<br> 1 Type of file, MFT96<br> 2 Wave type: R for Rayleigh <br> 3 Dispersion type: U for group velocity<br> 4 Mode: 0 represents the fundamental mode<br> 5 Filter period, T, in seconds<br> 6 Dispersion value, either group or phase<br> 7 Error in dispersion. This is just a place holder since there is no way to estimate an error from a single trace. The group velocity error is determined from the ratio of the filter period to travel time<br> 8 Distance in km<br> 9 Azimuth from the source to the receiver<br> 10 Spectral amplitude. <br> 11 Epicenter latitude <br> 12 Epicenter longitude<br> 13 Station latitude<br> 14 Station longitude<br> 15 control flag<br> 16 control flag<br> 17 Instantaneous period if this is preferred. This differs from the ilter period because the signal spectram is not flat.<br> 18 Comment: keyword<br> 19 Station <br> 20 Component<br> 21 Year<br> 22 Day of year<br> 23 Hour<br> 24 Minute - these identify the event origin time </p>
Time series of the correlation coefficient at seismic stations in the Mexican subduction zone
<p>The directory structure is as follows:<br> correlation_coefficient/[STATION]/[YYMMDD]</p> <p>Each file contains the time series of the correlation coefficient every 10 seconds for the data YYMMDD.</p>
Relocation of the 8 January 2022 Menyuan (Ms 6.9), China, earthquake sequence: A conjugated type of rupturing event with a seismic gap in the Haiyuan fault in northeast Tibet
<p>This file is the relocated data of the 2022 Menyuan Ms 6.9 and 2016 Menyuan Ms 6.4 earthquake using Double-difference algorithm. It is only used for scientific research.</p>
cross-correlations of seismic ambient noise - Liupan Shan
<p>Stacked cross-correlation functions of the continuous seismic data recorded at the stations around the Liupan Shan area. Data in Text format, named "LPS.stationA_stationB.ZZ".</p> <p>The format of the data is as follows:</p> <p>Lon (stationA) Lat (stationA) Elevation (stationA)</p> <p>Lon (stationB) Lat (stationB) Elevation (stationB)</p> <p>Time (t=0) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>Time (t=dt) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>Time (t=2dt) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>……</p>
Algorithm and evaluation results of three-dimensional seismic resilience
<p>The algorithm is based on genetic algorithm and improved genetic algorithm. The calculation results are detailed in the Excel file</p>
Global Seismic Hazard Map
<p>The Global Earthquake Model (GEM) Global Seismic Hazard Map (version 2023.1) depicts the geographic distribution of the Peak Ground Acceleration (PGA) in terms of fraction of the acceleration of gravity, with a 10% probability of being exceeded in 50 years, computed for reference rock conditions (shear wave velocity, Vs30, of 760-800 m/s). The map was created by collating maps computed using national and regional probabilistic seismic hazard models developed by various institutions and projects, in collaboration with GEM Foundation scientists. The OpenQuake engine, an open-source seismic hazard and risk calculation software developed principally by the GEM Foundation, was used to calculate the hazard values. A smoothing methodology was applied to homogenise hazard values along the model borders (Pagani et al., 2018). The map is based on a database of hazard models described using the OpenQuake engine data format (NRML); those models implemented initially in other software formats were converted into NRML. While translating these models, various checks were performed to test the compatibility between the original and new results computed using the OpenQuake engine. Overall the differences between the original and translated model results are small notwithstanding some diversity in modelling methodologies implemented in different hazard modelling software. Some areas in the map (e.g. Greenland) are currently not covered by an openly accessible hazard model. Due to possible model limitations, regions portrayed with low hazard may still experience potentially damaging earthquakes. The raster is prepared by interpolating values calculated at points with ~6 km spacing using inverse distance weighting of nearest neighbours. The raster values will differ most from these original values in areas where hazard changes rapidly.</p> <p>Technical details on the compilation of the hazard maps and the underlying models - including updates to model components made by GEM - are available at https://hazard.openquake.org/</p>
Seismic Signature of the Upper Continental Crust: Implications from the thermoelastic properties of Liebermannite and K-hollandite II
<p>This repository contains data used in "Seismic Signature of the Upper Continental Crust: Implications from the thermoelastic properties of Liebermannite and K-hollandite II".</p>
Safety and Effects of Implanted (Autologous) Skeletal Myoblasts (MyoCell) Using an Injection Catheter = SEISMIC Trial
ClinicalTrials.gov study NCT00375817. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.