Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
781
datasets available to search
ShareScore release 0.9.0
Dataset results
781 results for “earthquakes”
Ground deformation maps of the Visso and Norcia 2016 earthquakes captured from Sentinel-1 SAR
<p>The dataset contains the Line Of Sight (LOS) deformation maps obtained by applying Differential SAR Interferometry (DinSAR) to Sentinel-1 Interferometric Wide-swath (or TOPSAR) imagery.</p> <p>Two maps are provided: the first maps is the LOS deformation estimated from ascending data, the second map is the LOS deformation from descending images.</p> <p>Maps are expressed in metres and are provided in raster geotiff format.</p> <p>Data have been processed with GAMMA Interferometric processor.</p> <p>Ascending data details:</p> <p>SAR pairs are dated 2016/10/27 and 2016/11/02, acquired on relative orbit number 44, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 1x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 20 m.</p> <p>Descending data details:</p> <p>SAR pairs are dated 2016/10/26 and 2016/11/01, acquired on relative orbit number 22, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 2x10 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 40 m.</p>
Ground deformation maps of the Visso and Norcia (Italy) 2016 earthquakes from ALOS-2 SAR data
<p>The datasets consist of the Line of Sight (LoS) deformation maps obtained by applying Differential SAR Interferometry (DinSAR) to ALOS-2 (Strip Map acquisition mode) pairs.</p> <p>ALOS-2 is operated by the Japan Aerospace Exploration Agency (JAXA).</p> <p>Two maps were retrieved by processing data from both the ascending and descending track.</p> <p>Both the maps are in meters and provided in raster geotiff format.</p> <p>Data have been processed using the Sarscape© software.</p> <p>Ascending pair details:</p> <p>SAR images were acquired on 2016/08/24 and 2016/11/02, path 197, frame 850.</p> <p>The adopted DEM used for the topographic phase removal was the SRTM 1 arc second. The interferogram was generated by applying a 11x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product were geocoded in UTM WGS84 33 Nord projection, and a final pixel size of 40 m.</p> <p>Descending pair details:</p> <p>SAR images were acquired on 2016/08/31 and 2016/11/09, path 92, frame 2750.</p> <p>The adopted DEM used for the topographic phase removal was the SRTM 1 arc second. The interferogram was generated by applying a 11x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product were geocoded in UTM WGS84 33 Nord projection, and a final pixel size of 40 m.</p>
Strain and slip data for Kinematic inversion of fault slip during the nucleation of laboratory earthquakes
<p>The file contains the timeseries of strain and average slip obatined from a 8 gauges array used to monitor an injection experiment on a saw-cut centimetric scale sample loaded in a triaxial cell, under 30 MPa, 60 MPa and 90 MPa of confining stress.</p>
Mesh and large input files of SeisSol models of the Kahramanmaraş earthquake doublet published in Jia et al. (2023) and Gabriel et al. (2023)
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>This dataset contains mesh and large input files for SeisSol models of the Kahramanmaraş earthquake doublet, as published in Jia et al. (2023) and Gabriel et al. (2023). The rest of the setup can be found at: <a href="https://github.com/Thomas-Ulrich/Turkey-Syria-Earthquakes/" target="_new" rel="noopener">GitHub - Turkey-Syria Earthquakes</a>. <ul> <li> <p><strong>Turkey78_75_dip70_3</strong>: The mesh file.</p> </li> <li> <p><strong>Turkey78_75_dip70.smd</strong>: The SimModeler file used to generate the mesh.</p> </li> <li> <p><strong>Turkey78_75_dip70_3.xml</strong>: Contains the mesh and analysis attributes required to generate it using PUMGen and SimModelerLib.</p> </li> <li> <p><strong>Turkey78_75_dip70_3.xmdf</strong>: A helper file for opening the mesh in ParaView.</p> </li> <li> <p><strong>StressChange_filt_v7_joint_ASAGI.nc</strong>: The stress change from the kinematic model used in Jia et al.'s simulations.</p> </li> <li> <p><strong>Turkey_31M_o5_el_ev1_2500_s2_05_al065_R056_resampled_stress_change_coarse.nc</strong> and<br><strong>Turkey_31M_o5_el_ev1_2500_s2_05_al065_R056_resampled_stress_change_fine.nc</strong>: These files represent the stress change from the first event for the second event simulation in Jia et al.'s model. They were generated from the output files of the first event simulation, as described here: <a href="https://github.com/Thomas-Ulrich/Turkey-Syria-Earthquakes/tree/main/SeisSolSetupHeterogeneities/asagi_file" target="_new" rel="noopener">SeisSol Setup Heterogeneities - ASAGI Files</a>.</p> </li> </ul> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Quantifying the erasure of earthquake surface ruptures from desert landscapes: Implications for seismic hazard assessment
<p><strong>Original Landscapes</strong></p> <p>DEMs of ~120x140m landscapes clipped from:</p> <p>R1-10 = 2019 M7.1 Ridgecrest earthquake, 2019 lidar (Hudnut et al., 2020), and </p> <p>E1-10 = 2010 M7.2 El Mayor-Cucapah earthquake, 2010 lidar (OpenTopography, 2010).</p> <p>Example: "E5.asc"</p> <p> </p> <p><strong>Degraded Landscapes</strong></p> <p>Linearly diffused using <em>Landlab </em>(Hobley et al., 2017; Barnhart et al., 2020) at timesteps (100, 1000, 5000, 10000 yr) using a <em>k</em> of 1 m^2/kyr.</p> <p>Example: "e5_1000_001_eroded.asc"</p> <p> </p> <p><strong>Mapped Faults Shapefiles </strong>- E1_10_shps & R1_10_shps</p> <p>Faults mapped on each degraded landscape using a systematic mapping process (Scott et al., 2023; Adam, 2023)</p> <p> </p> <p><strong>Ridgecrest DEM</strong> - rc_7_1_0424_utm.tif</p> <p>0.014 m/pix DEM of a portion of the 2019 M7.1 Ridgecrest earthquake rupture, from 6 April 2024. Created from Structure from Motion using drone images. </p> <p> </p> <p><strong>Degradation and analysis python code</strong> - landscape_evolution_earthquake_ruptures-main.zip</p> <p>A set of scripts to simulate the effect of surface processes on surface ruptures and quantify the information loss associated with landscape evolution over time. Includes options to simulate surface processes with linear and non-linear diffusion, implemented using open-access code landlab.</p> <p> </p> <p><strong>References</strong></p> <p>Adam, R. (2023). Evaluation of remote mapping of active fault traces. Arizona State University.</p> <p>Barnhart, K.R., Hutton, E.W.H., Tucker, G.E., Gasparini, NM., Istanbulluoglu, E., Hobley, D.E.J., Lyons, N.J., Mouchene, M., Nudurupati, S.S., Adams, J.M., Bandarogoda, C., 2020, Short communication: Landlab v2.0: A software package for Earth surface dynamics: Earth Surface Dynamics Discussions, doi: 10.5194/esurf-2020-12.</p> <p>Hobley, D.E.J., Adams, J.M., Nudurupati, S.S., Hutton, E.W.H. Gasparini, N.M., Istanbulluoglu, E., and Tucker, G.E., 2017, Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics: Earth Surface Dynamics, v. 5, n. 1, p. 21-46, doi: 10.5194/esurf-5-21-2017.</p> <p>Hudnut, K.W., B. Brooks, K. Scharer, J.L. Hernandez, T.E. Dawson, M.E. Oskin, R. Arrowsmith, C.A. Goulet, K. Blake, M.L. Boggs, S. Bork, C.L. Glennie, J.C. Fernandez-Diaz, A. Singhania, D. Hauser, S. Sorhus (2020). 2019 Ridgecrest, CA Post-Earthquake Lidar Collection. National Center for Airborne Laser Mapping (NCALM). Distributed by OpenTopography. https://doi.org/10.5069/G9W0942Z.. Accessed: 2024-11-25 </p> <p>Opentopography; El Mayor-Cucapah Earthquake (4 April 2010) Rupture LiDAR Scan. Distributed by OpenTopography. https://doi.org/10.5069/G9TD9V7D . Accessed: 2024-11-25</p> <p>Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., Gray, B., Koehler, R., Thompson, S., Sarmiento, A., Dawson, T., Kottke, A., Young, E., Williams, A., Kozaci, O., Oskin, M., Burgette, R., Streig, A., Seitz, G., … Ingersoll, S. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. Geosphere. https://doi.org/10.1130/GES02611.1</p>
Rheological structure and lithospheric stress interaction in the Alaska subduction zone gleaned from the 2018 Mw 7.9 oceanic crustal earthquake
<p>This repository contains the observed and modeled first 2-year timeseries of postseismic deformation at GPS sites in the best-fit model associated with the 2018 Mw 7.9 Kodiak, Alaska earthquake (Timeseries.rar), as well as the preferred afterslip on the fault (Afterslip.rar).</p>
Coseismic frictional heating with concomitant hydrothermal fluid circulation revealed by rock magnetic properties of fault rocks from the rupture of the 2008 Wenchuan earthquake, China
<p>This repository contains the rock magnetic data associated with the manuscript entitled "Coseismic frictional heating with concomitant hydrothermal fluid circulation revealed by rock magnetic properties of fault rocks from the rupture of the 2008 Wenchuan earthquake, China" by Yan et al. published in <i>Geochemistry, Geophysics, Geosystems, </i>24, e2023GC011223. https://doi.org/10.1029/2023GC011223</p>
Data from: Friction and instability of glaucophane gouges at blueschist temperatures support abundance of intermediate-depth earthquakes
<p>Fluid release from the hydraulically-confined dehydration of blueschist minerals may significantly elevate fluid pressures and concomitantly reduce effective stresses. This is a viable mechanism to explain brittle failure and frictional instability, and it is a possible explanation for intermediate-depth earthquakes in cold subduction zones. We examine this hypothesis for glaucophane – a key index mineral for blueschist facies – at lower confining stresses where behavior is poorly understood. We conduct laboratory shear tests on glaucophane gouge at temperatures of 100–500<em>℃</em> and effective normal stresses of 50–200 <em>MPa</em>, to explore the controls of temperature, stresses and excess pore fluid pressures on fault friction. Frictional strength of glaucophane gouge at representative temperatures and stresses is ~0.70 and insensitive to temperature with a slight increase in friction coefficient at lower effective stresses. Elevating temperature promotes a transition from velocity-strengthening to mild velocity-weakening behavior, indicating the destabilizing effect of high-temperature downdip in subduction zones. Reducing effective normal stress or concomitantly elevating pore fluid pressure, potentially sourced from dehydration reactions, further strengthens the velocity-weakening response and would be manifest as moderate-sized earthquakes. This observed instability at higher temperatures or lower stresses is indexed with and accompanied by denser distributions of strongly- to moderately-localized shears in the microstructures – congruent with the observed mechanical response. Our results support the potential for enhanced unstable sliding of glaucophane gouges at lower effective stresses and at blueschist facies temperatures - and have significant implications for understanding the abundance of intermediate-depth earthquakes apparent in cold subduction zones.</p>
Earthquakes used for spllitting analysis
<p>These files include the list of earthquakes which were used for splitting analysis. </p><p>Columns 1 and 5 represent occurrence date and time of the earthquake, respectively.</p><p>Columns 2, 3, and 4 are the longitude, latitude, and depth of the epicenter, respectively, and column 17 is the magnitude.</p><p>Columns 8 and 12 are the estimated delay time and fast direction, respectively, and columns 9 and 13 are their standard errors.</p>
Data set for Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions
<p>Data set for manuscript "Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions" submitted to Geophysical Research Letters.</p>
Tōhoku earthquake - 2011
The Tōhoku earthquake (9.1 magnitude) hit Japan on March 11, 2011 for a duration of 5 minutes, and was followed by a tsunami which claimed the life of close to 16.000 people. The seismic events shown took place on that same day, between 01h41 and 23h59 (GMT). At 0.5X animation speed, 1 second of animation = 1 hour in real time. The main earthquake happened at 05h46 (1:80 mark in the animation), and slowing down the animation speed to 0.1X or keeping manual control around this event should allow you to better understand the aftershocks propagation. Data: * Topography and bathymetry: [Etopo1 Global Relief Model](https://www.ngdc.noaa.gov/mgg/global/global.html) * Earthquakes: [ANSS Comprehensive Earthquake Catalog](https://earthquake.usgs.gov/data/comcat/) * Fault plane: [Slab2: A Comprehensive Subduction Zone Geometry Model](https://doi.org/10.5066/F7PV6JNV) Created with python and blender ( and love :) ) *NEXT: add focal mechanisms, better metadata, funkier colormaps and superimpose historical maps?* Source: Objaverse 1.0 / Sketchfab
InSAR result of Cianjur Earthquake (November 2022), Indonesia
<p>This is the InSAR result of Sentinel-1 SAR data. We used three pairs of Sentinel-1 SAR data to produce the unwrapped phases and phases which were converted to KMZ format and can be opened using Google Earth Pro or QGIS software. The data covers the western part of Java Island, Indonesia </p>
A decade of short-period earthquake rupture histories from multi-array back-projection
<p>Data Set S1: *.bp files containing the short-period earthquake rupture patterns, energy radiated maps, and source time functions.</p> <p>Data Set S2: Earthquake source information and rupture parameter estimates in machine-readable format (*.csv file) based on visually determined rupture end times.</p> <p>Data Set S3: Rupture parameter estimates in machine-readable format (*.csv file) based on automatic rupture end times.</p>
Source data for graphs and charts used in the paper "A machine learning-based estimator for real-time earthquake ground-shaking predictions in Southern California"
<h1>DESCRIPTION:</h1> <h3>This repository contains the source data for graphs and charts used in the paper "A machine learning-based estimator for real-time earthquake ground-shaking predictions in Southern California" submitted and accepted at "Communications Earth & Environment journal" </h3> <h3>Marisol Monterrubio-Velasco, Scott Callaghan, David Modesto, Jose Carlos Carrasco, Rosa M. Badi , Pablo Pallares, Fernando Vázquez-Novoa, Enrique S. Quintana-Ortı́, Marta Pienkowska, and Josep de la Puente</h3> <h2><strong>DATA FOR FIGURES: </strong></h2> <h3><strong>Figure 1 : </strong></h3> <p>The data used in this figure comes from the CyberShake Study 15.4. Seismogram, intensity measure, and duration data from CyberShake Study 15.4 is available through the SCEC CyberShake Study 15.4 Globus Collection, served by the University of Southern California's Center for Advanced Research Computing. Direct link:<a href="https://g-46eaba.a78b8.36fe.data.globus.org/ACTN/3886/PeakVals_ACTN_10_0.bsa">https://g-46eaba.a78b8.36fe.data.globus.org</a>."</p> <h3><strong>Figure 2:</strong></h3> <p><strong>Model evaluation on the validation dataset for T = 2s</strong></p> <p>1. Random Forest predictions using the optimized hyperparameters depth=30, n_estimators=30 for the validation dataset at T=2s</p> <p><a href="../records/10640493/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat?download=1&preview=1">y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat</a></p> <p>2. Artificial Neural Network predictions using the optimized hyperparameters 9layer and 256 neurons for the validation dataset at T=2s</p> <p><a href="../api/records/10640493/draft/files/Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv/content" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv</a></p> <p>3. True values for the validation dataset at T=2s</p> <p><a href="../records/10640493/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat?download=1&preview=1">y_true_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat</a></p> <h3><strong>Figure 3:</strong></h3> <p><strong>Error metrics obtained for each simulated scenario using:</strong></p> <p><strong>- Artificial Neural Networks</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_NN.csv</a></p> <p><strong>- Random Forest regressor</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_RF.csv</a></p> <p><strong>- ASK14 GMPE</strong></p> <p><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_2.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_3.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_5.0_GMPE.csv, </a><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_10.0_GMPE.csv</a></p> <h3>Figure 4:</h3> <p><strong>MLESmap RotD50 predictions on a validation event of magnitude 6.85</strong></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T2s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T2s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T2s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T3s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T3s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T3s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T5s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T5s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T5s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T10s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T10s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T10s_map_2748.csv</a></p> <h3>Figure 5:</h3> <p><strong>Spatial configuration of five historical earthquakes and BBP stations also including the coordinates of synthetic stations from the CS_15_4 study</strong></p> <p><a href="../api/records/10640493/draft/files/SyntheticStationsCoordinates_CS_15.4.csv/content" target="_blank" rel="noopener noreferrer">SyntheticStationsCoordinates_CS_15.4.csv, </a><a href="../api/records/10640493/draft/files/Whittier_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Whittier_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Northridge_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Northridge_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/North_Palm_Springs_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">North_Palm_Springs_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Landers_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Landers_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Hector_Mine_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Hector_Mine_BBP_sites.csv</a></p> <h3><strong>Figure 6:</strong></h3> <p><strong>RotD50 predictions for real events for the ‘inside’ stations </strong></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_10.csv, NN_layers9Northridge_IN_event_metrics-T_10.csv, NN_layers9Landers_IN_event_metrics-T_10.csv, NN_layers9Hector_Mine_IN_event_metrics-T_10.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_10.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_5.csv, NN_layers9Northridge_IN_event_metrics-T_5.csv, NN_layers9Landers_IN_event_metrics-T_5.csv, NN_layers9Hector_Mine_IN_event_metrics-T_5.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_5.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_3.csv, NN_layers9Northridge_IN_event_metrics-T_3.csv, NN_layers9Landers_IN_event_metrics-T_3.csv, NN_layers9Hector_Mine_IN_event_metrics-T_3.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_3.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_2.csv, NN_layers9Northridge_IN_event_metrics-T_2.csv, NN_layers9Landers_IN_event_metrics-T_2.csv, NN_layers9Hector_Mine_IN_event_metrics-T_2.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_2.csv</a></p> <h2>Supplementary material:</h2> <h3>Supplementary Fig 2</h3> <p><strong>Boxplots comparing ML models and "true" values</strong></p> <p><strong>- DNN</strong></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period3.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period5.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period10.0_ALL_Validation_log10.csv</a></p> <p>- RF</p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T3s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T5s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y</a><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">_pred_dislib_T10s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p>- TRUE VALUES FROM CYBERSHAKE SIMULATIONS</p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T3s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T5s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T10s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p> </p> <h3>Supplementary Fig 3</h3> <p><strong>Error metrics obtained for each simulated scenario:</strong></p> <p><strong>Artificial Neural Networks:</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_NN.csv</a></p> <p><strong> Random Forest regressor:</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_RF.csv</a></p> <p><strong>ASK14 GMPE</strong></p> <p><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_2.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_3.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_5.0_GMPE.csv, </a><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_10.0_GMPE.csv</a></p> <h3>Supplementary Fig 4</h3> <p><strong>Predictions for the Synthetic event of magnitude 7.45</strong></p> <p>RF_predictions_T2s_map_3.csv, ASK_14_prediction_T2s_map_3.csv , ANN_predictions_T2s_map_3.csv</p> <p>RF_predictions_T3s_map_3.csv, ASK_14_prediction_T3s_map_3.csv , ANN_predictions_T3s_map_3.csv</p> <p>RF_predictions_T5s_map_3.csv, ASK_14_prediction_T5s_map_3.csv , ANN_predictions_T5s_map_3.csv</p> <p>RF_predictions_T10s_map_3.csv, ASK_14_prediction_T10s_map_3.csv , ANN_predictions_T10s_map_3.csv</p> <h3>Supplementary Fig 5</h3> <p><strong>Predictions for the Synthetic event of magnitude 8.05</strong></p> <p>RF_predictions_T2s_map_1240.csv, ASK_14_prediction_T2s_map_1240.csv , ANN_predictions_T2s_map_1240.csv</p> <p>RF_predictions_T1240s_map_1240.csv, ASK_14_prediction_T1240s_map_1240.csv , ANN_predictions_T1240s_map_1240.csv</p> <p>RF_predictions_T5s_map_1240.csv, ASK_14_prediction_T5s_map_1240.csv , ANN_predictions_T5s_map_1240.csv</p> <p>RF_predictions_T10s_map_1240.csv, ASK_14_prediction_T10s_map_1240.csv , ANN_predictions_T10s_map_1240.csv</p> <h3>Supplementary Fig 6</h3> <p><a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Northridge_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Landers_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Hector_Mine_OUT_event_metrics-T_10.csv</a></p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_5.csv, NN_layers9Northridge_OUT_event_metrics-T_5.csv, NN_layers9Landers_OUT_event_metrics-T_5.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_5.csv</p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_3.csv, NN_layers9Northridge_OUT_event_metrics-T_3.csv, NN_layers9Landers_OUT_event_metrics-T_3.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_3.csv</p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_2.csv, NN_layers9Northridge_OUT_event_metrics-T_2.csv, NN_layers9Landers_OUT_event_metrics-T_2.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_2.csv</p> <p> </p> <h3>Supplementary Fig 7</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_3s_log10_Review_ALL.csv</p> <h3>Suplementary Fig 8</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_5s_log10_Review_ALL.csv</p> <p> </p> <h3>Suplementary Fig 9</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_10s_log10_Review_ALL.csv</p> <p> </p> <h3>Suplementary Fig 10</h3> <p><a href="../api/records/10640493/draft/files/HyperParameters_T2s_log10.csv/content" target="_blank" rel="noopener noreferrer">HyperParameters_T2s_log10.csv</a></p>
Aftershock Relocation of the November 21, 2022 Cianjur (West Java - Indonesia) Earthquake using Temporary Seismic Network
<p>Aftershock Relocation of the November 21, 2022 Cianjur (West Java - Indonesia) Earthquake using Temporary Seismic Network </p>
The upper crustal deformation field of Greece inferred from GPS data and its correlation with earthquake occurrence
<p>These are Tables S1 and S2 which accompany the Article "The upper crustal deformation field of Greece inferred from GPS data and its correlation with earthquake occurrence" by Konstantinos Chousianitis, Sotirios Sboras, Vasiliki Mouslopoulou, Gerasimos Chouliaras, and Dionissios T. Hristopulos.</p>
Calculated GNSS offsets for the 2016 Kaikōura earthquake
<p>Calculated offsets for the entire New Zealand GNSS network for the 2016 Kaikōura earthquake. If using this data please cite the following paper:</p> <p>Clark, K.J., Nissen, E.K., Howarth, J.D., Hamling, I.J., Mountjoy, J.J., Ries, W.F., Jones, K., Goldstien, S., Cochran, U.A., Villamor, P. and Hreinsdóttir, S., 2017. Highly variable coastal deformation in the 2016 MW7. 8 Kaikōura earthquake reflects rupture complexity along a transpressional plate boundary. <em>Earth and Planetary Science Letters</em>, <em>474</em>, pp.334-344.</p>
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>
Fault zone material files for dynamic rupture modeling of the 2019 Ridgecrest earthquakes
<p>This repository contains the material files used to add a low-velocity fault zone to a dynamic rupture model of the 2019 Ridgecrest sequence (Taufiqurrahman et al., 2023, Nature).</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>
ScienceDex guides
Understand access before you commit
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)
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.