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39 results for “Ground Motion”
SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey
<p><strong>SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey</strong></p> <p>A simulated ground motion dataset for ten earthquake scenarios in Turkey (seven real, three hypothetical earthquakes).</p> <p>Each scenario consists of 180 combinations for input parameters, and ground motions are simulated at 104 sites for each set of combinations, resulting in 18720 simulated ground motions for each earthquake and 187200 motions in total.</p>
Development and impact analysis of ground motion datasets for potential strong-to-great seismic scenarios in Chinese mainland
<p>地震动情景数据对于评估地震灾害损失至关重要,并且是协作式多学科地震风险分析和区域灾害预防的基础要素。这项研究根据地震灾害分区数据确定了 50 个潜在的地震成因位置,并得到了地质和地震学证据的支持。根据潜在的损坏程度选择了四个震级(Mw 6.5、Mw 7.0、Mw 7.5 和 Mw 8.0),最终建立了 200 个地震情景。该数据集包括峰值地面加速度 (PGA)、峰值地面速度 (PGV) 和地震强度,是使用之前在应急响应中验证的强大 GMPE 生成的。这为灾害预防、减灾和城市规划提供了有用的支持,使其适用于许多分析需求。</p> <p><strong><em>注意: </em> 这是对高风险地震断层地震情景的模拟,旨在帮助决策者采取主动措施来减轻潜在地震的影响。它还为相关研究人员提供了一组可用的数据资源。请不要使用这些数据来生成或传播错误信息!</strong></p>
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period.
California Peak Ground Motion Dataset
<p>Dataset of peak ground motion recordings from M3-M7 earthquakes in California from 2011-2022 compiled as part of USGS Award G21AP10284. Each row corresponds to a different ground motion record with the following field:</p> <ul> <li>evid: USGS ComCat event ID</li> <li>evmag: event magnitude</li> <li>evlon: event longitude</li> <li>evlat: event latitude</li> <li>evdep: event depth (km)</li> <li>net: network name of recording site</li> <li>sta: station name of recording site</li> <li>stlon: longitude of recording site</li> <li>stlat: latitude of recording site</li> <li>dist[km]: source-site distance in km</li> <li>pga[%g]: peak ground acceleration in %g</li> <li>pgv[cm/s]: peak ground velocity in cm/s</li> </ul> <p>Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
Ground motion amplification of the Santiago basin
<div> <div>Code that generates an approximate distribution of strong ground motion amplification in the Santiago Basin. A combination of two-dimensional Gaussians is used to resemble the distribution found by Pilz et al. (2011) [https://doi.org/10.1111/j.1365-246X.2011.05183.x].</div> <div>The parameters used were selected through trial and error until a distribution was obtained that roughly resembled those found by Pilz. This code requires the data available at https://doi.org/10.5281/zenodo.10050539.</div> </div>
Data from the article "Past large earthquakes influence future strong ground motion in subduction zones".
<p>Rupture and Ground motion data (version 2) in the central zone of Chile using kinematic seismic simulation data published in https://doi.org/10.1007/s11069-024-06651-9. The rupture process is derived from coupling and geometry data incorporated into the Heterogeneous Energy-Based method (https://doi.org/10.1515/geo-2022-0522).</p> <p>A video summarizing the data and results can be found in: <a href="https://youtu.be/VDIgko7ieEY?si=U_hoXBcO3OlM6XY8">https://youtu.be/VDIgko7ieEY?si=U_hoXBcO3OlM6XY8</a> <br><br>Please note that this is a revised version where the data code has been slightly modified compared to its previous version.</p>
Code and data repository for the role of topography, geotechnical layering, and attenuation on ground motion prediction
<p>Data and scripts to reproduce research on the influence of topography, geotechnical layer, and attenuation on ground motion prediction in Salton Trough.</p>
Supplementary dataset for " Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake"
<p>This is the data used in " Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake". The paper is currently under review.</p>
numerical data to accompany "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations"
<p>This is the numerical data to accompany the paper "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations". Please refer to the README.txt file for information about the individual datasets and archive files. </p>
Attenuation of Seismic Waves and Ground Motion Model of the Reykjanes Peninsula, Iceland
<p>Events.csv - Location of used events</p> <p>Amplit_A_HOR.csv, Amplit_A_VER.csv - Acceleration amplitudes (Horizonlat/Vertical) and parameters of inversion</p> <p>Amplit_V_HOR.csv, Amplit_V_VER.csv - Velocity amplitudes (Horizonlat/Vertical) and parameters of inversion</p> <p>Amplit_D_HOR.csv, Amplit_D_VER.csv - Displacement amplitudes (Horizonlat/Vertical) and parameters for inversion</p> <p><br><br></p> <p> </p>
Data and code for "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024
<p>This data and code is made available to support the article: "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024".</p> <p>Includes: TMOC method output tide correlation, Antarctic Peninsula grounding line, DInSAR data, TMOC Code.</p> <p> </p> <p><strong>For the data:</strong></p> <p>These data are made available to acompany the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. (2024)</p> <p>This datset contains:</p> <p>AP_TMOC_tide_correlation_2019_2020.tif - Significance adjusted tide correlation values for the TMOC method for 2019-2020 for the Antarctic Peninsula.</p> <p>AP_GL_TMOC_2019_2020.shp - A continuous grounding line made from TMOC data and British Antarctic Survey Coastline Data. Intended for use by others.</p> <p>AP_GL_TMOC_2019_2020_source.shp - A discontinuous grounding line made from TMOC data and British Antarctic Survey Coastline Data including the source of each line segment.</p> <p>The folder 'Interferograms' contains the DInSAR products used in the manuscript, sorted by Sentinel-1 frame</p> <p> </p> <p><strong>For the code:</strong></p> <p>This code is made available to support the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al.</p> <p>The authors take no responsibility for the quality of results derived using this code.</p> <p>This code is licensed under a Creative Commons Attribution 4.0 International Licence: http://creativecommons.org/licenses/by/4.0/</p> <p>external functions required:<br>geoimread - https://uk.mathworks.com/matlabcentral/fileexchange/46904-geoimread<br>polarstreo_inv - https://uk.mathworks.com/matlabcentral/fileexchange/32907-polar-stereographic-coordinate-transformation-map-to-lat-lon<br>CATS208 tide model and TMD 2.5 matlab toolbox - https://www.esr.org/research/polar-tide-models/tmd-software/</p> <p>The function TMOC_GL_v8 implements the TMOC method descibed in Wallis et al. 2024. This is a 'bring your own data' version.</p> <p>The script pp_folder prost-processes the outputs using the functuon LPfilt_cc</p> <p> </p>
The AlpArray-based ground motion visualization of teleseismic earthquakes
<p>We present a new comprehensive visual representation of global seismic phases using AlpArray. Our AlpArray-based animations connect spatial-temporal wavefield and time-dependent array method to visualize the evolution of teleseismic phases over time. Here are the animations of a few example teleseismic events occurred during the course of AlpArray (2016-2019).</p>
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
Synthetic ground motions to support the Fennoscandian GMPEs. Supporting information – Response spectra dataset in excel format
<p>This dataset has been created in the NKS project: ”Synthetic ground motions to support the Fennoscandian GMPEs”, contract: NKS-R(18)126/5. The data is RotD50 (Boore, 2010, doi: <a href="https://doi.org/10.1785/0120090400">10.1785/0120090400</a>), pseudo-acceleration response spectra calculated from synthetic ground motions generated using physics-based modeling of Fennoscandian earthquakes. The earthquake magnitude range is 4.3-5.6; the rupture distance range 2-30km and the hypocenter depth range 2-20km. The response spectra is in mm/s2 and should be used up to 25Hz.</p> <p>Cite the data as part of the research report: Fülöp, L., Jussila, V., Fälth, B., Voss, P., Lund, B. 2019. Synthetic ground motions to support the Fennoscandian GMPEs. NKS - Nordic Nuclear Safety Research NKS-424, ISBN: ISBN 978-87-7893-514-4</p> <p>Methods used to generate the data are described in:</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., and Heikkinen, P. 2017. Modelling as a Tool to Augment Ground Motion Data in Regions of Diffuse Seismicity – Final report. NKS - Nordic Nuclear Safety Research NKS-394, ISBN: 978-87-7893-482-6</p> <p>Fülöp, L., Jussila, V., Lund, B., Fälth, B., Voss, P., Puttonen, J., Saari, J., 2016. Modelling as a tool to augment ground motion data in regions of diffuse seismicity - Progress 2015. NKS Nordic Nuclear Safety Research, ISBN: 978-87-7893-448-2</p>
Ground motions for the Greater Wellington Region from selected synthetic earthquakes, modelled using OpenQuake
<p>Model results and plots forming an electronic supplement to the GNS Science Report: </p> <p>Howell A, Penney C, Kaiser AE, Fry B. 2023. Modelling ground motions in the Greater Wellington region from multi-fault earthquakes in central Aotearoa New Zealand. Lower Hutt (NZ): GNS Science. 19 p. (GNS Science report; 2023/45). https://doi.org/10.21420/Z9SM-0G27 . </p> <p>The folder includes:</p> <ul> <li>A CSV file containing summary information for each of the 20 modelled earthquakes.</li> <li>Plots of the slip distribution of each event.</li> <li>Plots of shaking (PGA, Sa(0.5) and (Sa(1.5) for each event).</li> <li>GeoTIFFs representing the crustal and subduction components of ground motions for each earthquake.</li> </ul> <p>Please read the report for details of the modelling and limitations of the approach.</p> <p> </p> <p> </p>
A Subset of CyberShake Ground Motion Time Series for Response History Analysis
<p>A subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response history analyses.</p> <p>v1.0.1: update readme file</p>
The datasets for the paper "The long-lived and recent seismicity at the lunar Orientale basin: Evidence from morphology and formation ages of boulder avalanches, tectonics and seismic ground motion" JGR: Planets (e2020JE006553)
<p>The datasets contain supporting files for the paper:</p> <p>Mohanty, R., Kumar, P.S., Raghukanth, S.T.G., & Lakshmi, K.J.P., (2020). The long-lived and recent seismicity at the lunar Orientale basin: Evidence from morphology and formation ages of boulder avalanches, tectonics and seismic ground motion, JGR: Planets, e2020JE006553.</p>
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Strong-ground motion for the city of Santiago (Chile) by using the Heterogeneous Energy-Based method
<p>Information pertaining to the generation of strong ground motion in the city of Santiago, Chile, using the Heterogeneous Energy-Based method proposed by Venegas-Aravena (2023) for the San Ramón Fault.</p>
Validation of Peak Ground Velocities Recorded on Very-high rate GNSS Against NGA-West2 Ground Motion Models: Dataset
<p>Contains all the data files for the manuscript of the same name.</p>
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