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106 results for “tsunami”
Aotearoa New Zealand RSQSim model outputs for tsunami modelling
<p>RSQSim model outputs to support the publication: "<strong>A Novel Method to Determine Probabilistic Tsunami Hazard using a Physics-based Synthetic Earthquake Catalog: a New Zealand Case Study</strong>" by Hughes et al. (2023; JGR, submitted). In many respects, these model outputs represent an updated version of the New Zealand RSQSim catalogue published by Shaw et al. (2022; <a href="http://doi.org/10.5281/zenodo.5534462">doi.org/10.5281/zenodo.5534462</a>).</p> <p>This archive contains:</p> <ol> <li>The outputs of an updated RSQSim synthetic earthquake catalogue for Aotearoa-New Zealand.</li> <li>A ~30kyr subset of the main catalogue, trimmed to a minimum M<sub>W</sub> of 7.0.</li> <li>Summary figures comparing the statistical properties of the synthetic catalogue to observations of New Zealand seismicity.</li> <li>Python scripts for visualizing the synthetic earthquake catalogue, and calculating seafloor displacements from synthetic earthquakes that can be used as tsunami model inputs.</li> </ol> <p>For more information, please refer to the publications cited above and/or the more detailed README files within the archive.</p>
Supplementary material for 3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami
<p>This repository contains the supplementary files for our SC21 submission: "3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami".</p> <p>It contains the input data for all simulations. For more details, please refer to the included README.md files.</p> <p> </p> <p>The directory "seissol-sc21-revision-source-code" contains the version of SeisSol that we used.</p> <p> </p>
Data regarding the application of the inverse modeling (TsuSedMod) to the sediment deposits of CE 1755 Lisbon tsunami at Salgados, Algarve (Portugal)
<p>This dataset contains data used to run TsuSedMod model. It contains the vertical textural distribution of 4 sediment samples (LV09, LV11,LV11a and LV13) retrieved at Salgados, southern Portugal<br> and respective results.</p>
Triggers and consequences of landslide-induced tsunamis – reconstruction of the Taan Fiord 2015 tsunami event
<p>Models codes are provided for some examples of the adopted modeling approaches.</p> <p>STL. files for the solid bodies are available in the zip file.</p> <p>A bathymetric map of the Taan Fiord on October 2015 and related contour shape file are provided.</p> <p>Values of the solid-fluid mixture density for different initial close packing volume fractions are shown in the table.</p> <p>A table summarizes the available model approaches, implemented in FLow 3D, useful in reproducing a Landslide-induced tsunami</p>
Ilustración "Memoria del tsunami 16 de septiembre de 2015, Coquimbo, Chile"
<p>Esta representación visual está inspirada en fotografías y testimonios que ilustran la memoria colectiva de los y las habitantes de Coquimbo que vivieron el terremoto y tsunami del 16 de septiembre de 2015 que afectó el humedal El Culebrón y los barrios aledaños: Avda. Baquedano (Barrio Baquedano, Población Victoria), Puerto de Coquimbo y Peñuelas.</p><p>Este desastre socio-natural impactó las costas desde Chañaral hasta Concón, generando daños tanto a nivel personal, material y emocional. La memoria evoca recuerdos de tristeza, destrucción, angustia, olvido y desolación, que se mezclan con acciones de unión, reconstrucción, apoyo, amor, y voluntad. Diversidad de experiencias y de emociones se manifiestan en el presente a partir del acontecimiento, el cual marcó un antes y un después para personas y barrios de la zona costera de Coquimbo. Y aunque en ocasiones, el recordar sea incómodo y doloroso, el ser conscientes de no olvidar y perpetuar testimonios y vivencias, nos permite aprender a reconocer, planificar, preparar y a convivir de mejor manera con nuestro entorno.</p>
Tsunami source model of the earthquake beneath Hyuganada Sea on 8 August 2024 estimated using ocean-bottom pressure gauge records of N-net and DONET
<p>This dataset contains the results obtained by the tsunami waveform inversion analysis of the offshore tsunami data recorded by the ocean-bottom pressure gauges of N-net and DONET. See README.txt for the detail of this dataset.</p>
Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):</div> <br> <div>Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha</div> <div> -<strong>pthaSR.tar.gz </strong>- max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <div> -<strong>pthaCT.tar.gz</strong> - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <br> <div>The filename follows the nomenclature with:</div> -true - simulation results</div> <div>-pred - emulator with pretraining</div> <div>-pred_direct - emulator without pretraining</div> <div>-pred_nodeform - emulator without pertaining and deformation input</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> </div> </div> <p> </p>
Processed files used for OQ based tsunami loss modelling using HPC based inundation and emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <div> </div> <div>The risk calculation and procedure is available in the main repo: <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk">https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk </a></div> <div> </div> <br> <div>The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):</div> <br> <div>Processed hazard information used to prepare and run OQ event based risk analysis are as below,</div> <div> -<strong>hazard.tar.gz </strong>- preliminary numpy files with sitcol, eventid and hazard magnitude info</div> <div> -<strong>loss.tar.gz</strong> -final hdf5 files with both event hazard and site info</div> <br> <div>The filename follows the nomenclature with:</div> </div> <div>ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk</strong></div> <div><strong>https://github.com/naveenragur/OQ-Tsunami</strong></div> </div> </div>
Geoclaw inputs and processed outputs used in building tsunami ML surrogates for nearshore and onshore approximation
<p>This dataset contains some input files needed for GeoClaw tsunami simulation and post-processed outputs used for training the nearshore and onshore surrogates discussed in the article - Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates available as preprint (https://doi.org/10.5194/nhess-2024-72) and project repo - https://github.com/naveenragur/tsunami-surrogates</p> <p>The GeoClaw simulation requires the following files: </p> <p><strong><a href="../api/records/10817116/draft/files/geoclaw_dtopo_files.tar.gz/content" target="_blank" rel="noopener noreferrer">geoclaw_dtopo_files.tar.gz</a></strong><strong>: </strong>input bathymetry and topography elevation in asc format from multiple sources of datasets.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_his.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_his.tar.gz</a></strong>: tsunami displacement(dtopo) input files in tt3 format for historic earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeA.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeA.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeB.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeB.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p>The machine learning training and testing requires:</p> <p><strong><a href="../api/records/10817116/draft/files/procesed_tsunami_data.tar/content" target="_blank" rel="noopener noreferrer">procesed_tsunami_data.tar</a>: </strong>the post-processed outputs for the three test location i.e. (1) the waveforms( time series recorded for the water level at the offshore and the nearshore gauge locations and (2) the maximum inundation depth recorded at the fixed grids onshore.</p> <p><a href="https://zenodo.org/uploads/14902165"><strong>tsunami-surrogates-nhess-2024-72.tar.gz</strong></a><a href="https://zenodo.org/uploads/14902165"> </a>: Model code, scripts and notebooks used in the study</p>
Spectral characteristics of ionospheric disturbances over the Southwestern Pacific from the January 15, 2022 Tonga eruption and tsunami
<p>This dataset contains the filtered ionospheric total electron content values used in the manuscript of the same name.</p>
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>
Data for figures of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō
<p>Data for figures in a planned publication of scenario-based study of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō.</p>
Simulation data for a semi-empirical approach for tsunami inundation in South Italy
<p>The archive contains data used for the simulations devised for a semi-empirical method for the evaluation of tsunami wave propagation and inundation along the coasts of South Italy.</p> <p>UPDATE - October 19, 2022<br> - Added readme files (in English and Italian) with an explanation of the files of the repository.<br> - Added text files for values of alpha also for the case of perpendicular tsunami fronts (as per Figures 5d and 5f of the paper); only files for realistic tsunami fronts (as per Figures 5c and 5e of the paper) were present in the previous version of the repository. </p>
Effects of earthquake spatial slip correlation on variability of tsunami intensity variables
<p>Data entry to reproduce the tsunamis with the aid of GeoClaw, as well as the spatial earthquake slip rupture.</p>
Figure 2 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 2. – Schematic illustration of the framed seine net used in the present study.
2010 Mentawai Tsunami Earthquake Simulations
<p>This dataset includes rupture models and associated waveforms of the 2010 M7.8 Mentawai, Indonesia tsunami earthquake. The simulations were generated using codes from https://github.com/UO-Geophysics/MudPy.</p> <p><strong>Test_simulations</strong> contains simulations for the initial tests individually varying the rise time, rupture velocity (Vrupt) and stress-drop (Figs. 5-7 in the accompanying publication).</p> <p><strong>GPR_simulations </strong>contains simulations for the Gaussian process regression analysis. These are separated into strong-motion waveforms for the 1D analysis with varying stress drop and HR-GNSS waveforms for the 2D analysis with varying rise time and Vrupt (Fig. 8 in the accompanying publication).</p> <p><strong>TsE_simulations</strong> contains the final datatset generated using recomended tsunami earthquake parameters (Table 1 and Fig. 9 in the accompanying publication). </p> <p>For the GNSS waveforms, files within <strong>GNSS_raw</strong> are the raw GNSS waveforms without noise, and files within <strong>GNSS_noisy</strong> are the raw waveforms with real noise added. No noise was added to the strong motion waveforms.</p> <p>The .log and .rupt files within the /ruptures/ subdirectories are standard files generated from MudPy. The .log files are summaries of each rupture, and the .rupt files are the full rupture kinematics of each rupture including slip information for the individual subfaults.</p> <p>Details on how the ruptures and data were generated are available in the following publication:</p> <p>Nye, T. A., Sahakian, V. J., & Melgar, D. (2024). Validation of tsunami earthquake ground-motion simulations using near-field observational data: Rupture parameter constraints from the 2010 Mentawai event. Seismica, 3(2). https://doi.org/10.26443/seismica.v3i2.1152</p> <p>*This work was supported by NASA ROSES grant 80NSSC21K0841 and USGS grants G19AP00071 and G23AP00048.</p> <p> </p>
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
GeoClaw input files of the 2004 Sumatra-Andaman tsunami scenarios published in Ulrich et al. (2021)
<p>This dataset contains the input files of the GeoClaw scenarios of the 2004 Sumatra-Andaman tsunami presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.</p> <p><strong>runconverterLMU_WGS84.sh</strong>: contains all the steps to transform a SeisSol surface output to a Geoclaw tt3 file.<br> It uses the displacement converter of Samoa to rasterize a SeisSol surface output to NetCDF.<br> See <strong>README_build_displacement-converter.txt</strong> for the procedure to download and build the displacement converter.<br> Note that the SAMPLER (<a href="https://github.com/SeisSol/SAMPLER">https://github.com/SeisSol/SAMPLER</a>) will replace the displacement-converter in the future.<br> <br> <strong>convert_geographic_SeisSol_geom.py</strong>: to transform the geometry array of a SeisSol surface output file to the geocentric coordinate system (latitude, longitude).<br> <strong>tapperNetcdf.py</strong>: to apply a Hanning window on a NetCDF file. This prevents sharp displacement discontinuities at the limits of the region of imposed displacements, which could generate spurious waves.<br> <strong>convert_netcdf_tt3.py</strong>: to convert a NetCDF displacement file to the tt3 format (GeoClaw).<br> <br> The GeoClaw simulations require the following files:<br> <br> <strong>displacement_tt3_files.tar.gz</strong> : rasterized input files in tt3 format for 4 earthquake scenarios.<br> <strong>gebco_2019_n25.0_s-21.0_w55.0_e110.0.nc</strong>: input bathymetry and topography data in NetCDF format downloaded from https://www.gebco.net/.<br> <strong>setrun.py</strong> which defines the simulation parameters.<br> a Makefile, plateform specific see e.g. https://github.com/clawpack/geoclaw/blob/master/examples/tsunami/chile2010/Makefile<br> <strong>setplot_fig4.py</strong>: configures GeoClaw for generating outputs for figure 4. <br> <strong>setplot_animation.py</strong>: configures GeoClaw for generating outputs for the supplementary animations. <br> GeoClaw simulations are run with `make .plots`.</p>
Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique) - DATASETS
<p>Datasets of the 6 presented experiments with 4mm beads presented in the paper "Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique)".</p> <p>Each datasets (csv file) corresponds to the hand picked profile of either the water free surface or the granular material at 0.1 second of interval. </p> <p>The third dataset for each experiment corespond of the gauges records.</p>
Model data of tsunami and storm surge scenarios for Anegada, British Virgin Islands
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