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781 results for “earthquakes”
Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET)
<p>This dataset contains local earthquake coda waveform and pre-signal noise waveforms from the Jammu And Kashmir Seismological NETwork (JAKSNET), a joint endeavor between the Indian Institute of Science Education and Research Kolkata (IISER-K), Shri Mata Vaishno Devi University (SMVD) and the University of Cambridge, UK. The network was initiated in July 2013 and comprised 24 broadband seismograph systems deployed across the J&K Himalaya. A total of 696 vertical component coda waveforms, from 121 small-to-moderate local earthquakes of magnitude between 3.0 and 5.5, within the epicentral distance of 200 km are provided in this database. Pre-signal representative noise from 22 stations, which recorded these earthquakes, have been provided for computing signal-to-noise ratio for the coda signal. The waveform data is sampled at 100 samples per second (sps), are corrected for instrument response, and filtered in the frequency band of 0.02 to 30 Hz. Coda waveforms start from twice the S-wave arrival time and are of 90 s duration. This data has been used to compute the seismic coda-wave attenuation of the Jammu and Kashmir Himalaya. The manuscript is submitted for review in JGR Solid Earth and this dataset complements the manuscript. </p>
A high-resolution earthquake catalog for the Tony Creek dual Microseismic Experiment (ToC2ME)
<p>Location/detection method: In this study, we revisit the continuous recordings that are acquired by 69 three-component nodes at a Hydraulic Fracturing (HF) site in Alberta, Canada, taking advantage of a machine learning-based seismic detection and location workflow (LOC-FLOW). This workflow integrates four key steps to generate earthquake catalogs, including phase picking using neural network (PhaseNet), rapid association of seismic phases (REAL), seismic location (e.g., VELEST) as well as relocation (e.g., hypoDD). The catalog contains 21,619 earthquakes with magnitudes down to -2 and achieves a high location accuracy at meter scale. The improved earthquake locations allow us to better delineate the fractures and faults activated during the HF. Our results further reveal the complexity of the triggering mechanism of HF-induced earthquakes according to the statistical analysis of the frequency-magnitude distribution of earthquakes. Our study suggests that multiple factors, including pore fluid pressure due to fluid injection and perturbation in the stress field caused by earthquakes, can jointly control the spatiotemporal variations of regional seismicity during the multi-stage HF operation. </p>
3D locations of the radiators and the details of Coulomb stress about the Mw 7.3 East Cape earthquake
<p>This is part of the supplementary material of the paper " The 2021 Mw 7.3 East Cape earthquake: Triggered Rupture in Complex Faulting Revealed by Multi-Array Back-projections". The 3D locations of the radiators are in the file ‘S1.xlsx’. The details of Coulomb stress calculation are in the file ‘S2.xlsx to S9.xlsx’. </p>
Source model inputs and results - The July 2022 Mw 7.0 Northwestern Luzon Earthquake, Philippines
<p>This repository includes all the modelling inputs necessary to reproduce the results presented in 'Source Model and Characteristics of the 27 July 2022 M<sub>W</sub> 7.0 Northwestern Luzon Earthquake, Philippines' by Rimando et al. (2022) as follows: the fault geometries ('custom_fault_dip30_vaf,' 'custom_fault_dip30_abra'), the downsampled InSAR LOS deformation input ('statics'), the crustal model ('crust01'), and the run file which includes all the run parameters that were used ('luzon_run_clean'). </p> <p>Also included are the main outputs ('Abra_Results' and 'Vigan_Results') that Mudpy should produce using the abovementioned input files.</p> <p>Once an interested party downloads MudPy (MudPy v.1.0 was used for this study: https://github.com/dmelgarm/MudPy), these folders just have to be placed in their spots in the directory structure (outlined at https://github.com/dmelgarm/MudPy/wiki/) in order to reproduce the findings in Rimando et al. (2022).</p> <p><br> </p>
Dataset of the 1976 Ms 7.3 Chaldiran earthquake
<p>The dataset, used in the study of the 1976 Ms 7.3 Chaldiran earthquake by Lu and Zhou (2023), includes KeyHole (KH-4B and KH-9) ortho-images, post-earthquake Pleiades data (1-m DEM and ortho-image) and a 30-m ALOS DEM.</p>
Foraminiferal insights into the complexities of the turbidity currents triggered by the 2016 Kaikoura Earthquake, New Zealand, Supplementary Appendices
<p>This data set and cluster analysis dendrogram support the published article.</p> <p>Supplementary Appendix 1. Measured foraminiferal data for 2016 Kaikōura turbidite and pre-turbidite samples. </p> <p>Supplementary Appendix 2. Cluster analysis dendrogram of foraminiferal samples based on the relative abundance of tests >125 µm of the key benthic “genera” (Table 2) using Bray Curtis similarity coefficient.</p>
Earthquake catalog for "Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway"
<p>This folder contains earthquake catalog used in "Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway" by Shiddiqi et al. (2022) submitted to Geophysical Journal International.</p>
Supplementary Material to: A secondary zone of uplift measured after megathrust earthquakes: caused by early downdip afterslip?
<p>This archive contains supplementary material to the publication "A secondary zone of uplift measured after megathrust earthquakes: caused by early downdip afterslip?"</p> <p>This archive is divided into two folders. One contains scripts and parameterization used for our subduction zone toy models, input files for use with the Pylith software, and slip optimization utilities. A second folder contains scripts and parameterization for our study of the 2010 Mw8.8 Maule (Chile) earthquake. Note that slip optimization utilities rely on the use of the Classic Slip Inversion python library (https://github.com/jolivetr/csi).</p>
Data and program codes to reproduce the results of local earthquake seismic tomography for Tenerife Island
<p>This file contains the files to reproduce the results presented in the article: <strong>Local earthquake seismic tomography reveals the link between the crustal structure and volcanism in Tenerife (Canary Islands) </strong>by Ivan Koulakov, Luca D'Auria, Janire Prudencio, Iván Cabrera-Pérez, Nemesio M. Pérez, Jesús M. Ibáñez, <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Tenerife Island, Canary Archipelago.</p> <p>3. README_TENERIFE.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Deep, shallow and surface fault-zone deformation during and after the 2021 Mw7.4 Maduo, Qinghai, earthquake illuminates fault structural immaturity
<p>These datasets include the postseismic InSAR time series on ascending and descending tracks and the relocated aftershocks (Wang et al., 2021) of the 2021 Maduo earthquake. The details can be found in our JGR paper.</p> <p>Reference</p> <p>Wang, W., Fang, L., Wu, J., Tu, H., Chen, L., Lai, G., & Zhang, L. (2021). Aftershock sequence relocation of the 2021 Ms7. 4 Maduo earthquake, Qinghai, China. Science China Earth Sciences, 64(8), 1371-1380.</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>
InSAR Interferograms and Products from 2021, M7.2 Nippes, Haiti earthquake (Curated Dataset)
<p>This dataset contains curated interferograms and derived products from the August 14, 2021 M7.2 Nippes, Haiti earthquake and accompanies the BSSA publication: <a href="https://doi.org/10.1785/0120220109" target="_blank" rel="noopener">https://doi.org/10.1785/0120220109</a>. Data products are also available via https://topex.ucsd.edu/haiti_7.2/index.html</p> <p>The data is organized in directories by satellite, relative orbit, and pair:</p> <p>├── ALOS1_A138<br>│ └── 20100116_20100603<br>├── ALOS2_A042<br>│ ├── 20210101_20210827<br>│ ├── 20210101_20211231<br>│ ├── 20210827_20211231<br>│ └── phasegrad<br>├── ALOS2_A043<br>│ ├── 20201223_20210818<br>│ └── phasegrad<br>├── ALOS2_D138<br>│ └── 20191210_20210817<br>├── S1_A004<br>│ ├── 20210805_20210817<br>│ ├── 20210817_20210823<br>│ ├── 20210823_20210829<br>│ └── 20210829_20210904<br>└── S1_D142<br> ├── 20210803_20210815<br> ├── 20210815_20210821<br> ├── 20210821_20210827<br> └── 20210827_20210902</p> <p>Each pair directory contains the relevant .grd files and HDF5 file, named according to the standard:</p> <p><SAT>_<SW>_<RELORB>_<FRAME>_<DATE1>-<DATE2>_<TBASE>_<BPERP>.h5</p> <p>We also include phase gradient .grd files, calculated in the range (xphase_mask_ll.grd) and azimuth (yphase_mask_ll.grd) directions.</p> <p>Please contact Zoe Yin (hyin@ucsd.edu) or Jennifer S. Haase (jhaase@ucsd.edu) with any questions. </p>
Earthquake Cycle Deformation Associated with the 2021 Mw 7.4 Maduo (Eastern Tibet) Earthquake: An Intrablock Rupture Event on a Slow-Slipping Fault from Sentinel-1 InSAR and Teleseismic Data
<p>Coseismic slip models of the 2021 Mw 7.4 Maduo (eastern Tibet) earthquake derived from Sentinel-1 InSAR and teleseismic data.</p> <p>Interseismic eastward and vertical velocity and maximum shear strain rate fields.</p> <p>Citations:</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data. Journal of Geophysical Research: Solid Earth, 127, e2022JB024268. <span>https://</span>doi.org/10.1029/2022JB024268</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7215161<span>.</span></p>
InSAR time-series and FEM Model of the Post-seismic Surface Deformation following the 2013 Baluchistan Earthquake
<p>Subduction zone accretionary prisms are commonly modeled as elastic structures where permanent deformation is accommodated by faulting and folding of otherwise elastic materials, yet accretionary prisms may exhibit other deformation styles over relatively short time scales. In this study, we use 6.5-year (2014-2021) Sentinel-1 InSAR time-series of post-seismic deformation in the Makran accretionary prism of southeast Pakistan to characterize non-linear viscoelastic deformation within an active accretionary prism on short timescales (months to years). We constructed a series of 3-D finite-element models of the Makran subduction zone, including an accretionary prism, and constrained the elastic thickness of the upper wedge and the flow-law parameters (power-law exponent, activation enthalpy, and pre-exponential constant) of the lower wedge through forward model fits to the InSAR time-series. Our results show that the prism is elastically thin (8-12 km) and the non-linear viscoelastic relaxation of the deep portions of the prism alone can sufficiently explain the post-seismic surface deformation. Our best fitting flow-law parameters (<em>n</em> = 3.76±0.39, <em>Q</em> = 82.2±37.73 kJ mol<sup>-1</sup>, and <em>A</em> = 10<sup>-3.36±4.69</sup>) are consistent with triggering of low temperature dislocation creep within fluid-saturated siliciclastic rocks. We believe that the fluids necessary for this weakening originate from sedimentary underplating and/or the presence the hydrocarbons. The presence of power-law rheology within the lower wedge impacts the estimated plate coupling and the stress state in the subduction system, with respect to the conventional elastic wedge model, and hence need to be considered in future earthquake cycle models.</p>
Supporting Data for pyCSEP: An Enhanced Python Toolkit for Earthquake Forecast Developers
<p>Contains the reproducibility package for pyCSEP: An Enhanced Python Toolkit for Earthquake Forecast Developers</p> <p>Please view the README.md in this archive for instructions on how to run the reproducibility package.</p> <p>The source code for pyCSEP used in this reproducibility package can be viewed on GitHub <a href="https://github.com/SCECcode/pycsep/releases/tag/v0.5.2">here</a>.</p>
Data for "A benchmarking method to rank the performance of physics-based earthquake simulations"
<p>This repository contains the datasets and codes supplementary to the article "<strong>A benchmarking method to rank the performance of physics-based earthquake simulations</strong>" submitted to <em>Seismological Research Letters</em>.</p> <p>The datasets include the codes to run the ranking analyses, inputs and outputs for the RSQSim earthquake simulation cases explained in the paper: a single fault and the fault system of the Eastern Betics Shear Zone (simulations from Herrero-Barbero et al. 2021). The results and data are stored in a separate folder for each case study presented in the paper: "Single fault" and "EBSZ". Each folder contains a series of subfolders and a Python script to run the ranking analysis for that specific case study. The script contains the default path references to read all necessary input files for the analysis and automatically save all the outputs. The subfolders are:</p> <p><strong>./Inputs: </strong>This folder contains the input files required for the RSQSim simulations. This includes:</p> <p>a. The fault model ("Nodes_RSQSim.flt" and "EBSZ_model.csv" for the single fault and EBSZ cases, respectively), which specifies the coordinate nodes of the fault triangular meshes and fault properties such as rake (º) and slip rate (m/yr).</p> <p>b. Neighbor file ("neighbors.dat"/"neighbors.12") that contains lists of triangular patches of the fault model that are neighboring. This file is used in RSQSim.</p> <p>c. Input parameter file ("Input_Parameters.txt"): this file specifies the parameters that are variable in each catalogue. This file is just for information purposes and is not used for the calculations.</p> <p>d. Parameter file(s) to run the RSQSim calculations.</p> <p>*For the single fault, this file is common ("test_normal.in") and is updated during the calculation when executing the "Run.sh" file in the terminal when running RSQSim. This file contains a script that loops through the input parameters a, b and normal stress explored in the study and changes the input parameter file accordingly in each iteration.</p> <p>*For the EBSZ, this file is specific for each simulation ("param_EBSZ_(n).in"), as each simulation was run separately.</p> <p>e. (Only for the EBSZ case) Input paleoseismic data for the paleorate benchmark. One file ("coord_sites_EBSZ.csv") contains a list of UTM coordinates of each paleoseismic site in the EBSZ and another ("paleo_rates_EBSZ.csv") contains the mean recurrence intervals and annual paleoearthquake rates in those sites (data from Herrero-Barbero et al., 2021).</p> <p><strong>./Simulation_models:</strong> contains several subfolders, one for each simulated catalogue (96 for the single fault case and 11 for the EBSZ). Each subfolder contains data that is read by the ranking code to perform the analysis. </p> <p>*For the single fault, the folder names follow the structure "model_(normal stress)<em>(a)</em>(b)". </p> <p>*For the EBSZ, the folder names are "cat-(n)".</p> <p><strong>./Ranking_results: </strong>contains the outputs of the ranking analysis, which are two figures and one text file.</p> <p>*Figure 1 ("Final_ranking.pdf"): visualization of the final ranking analysis for all models against the analyzed benchmarks.</p> <p>*Figure 2 ("Parameter_sensitivity.pdf"): visualization of the final and benchmark performance versus the input parameter of the models.</p> <p>*Text file ("Ranking_results.txt"): contains the final and benchmark scores of each simulation model. This file is outputted so the user can reproduce and customize their own figures with the ranking results.</p> <p>To use the ranking codes in you own datasets, please replicate the folder structure explained above. Use the code that best suits your data: use the one for the single fault if you wish not to use the paleorate benchmarks, and use the EBSZ one if you wish to include these data in your analysis. At the beginning of the respective codes (before the "Start" block comment) you will find the variables where the file names of the fault model and paleoseismic data are indicated. Change them to adapt it to your data. There you can also assign weights to the respective benchmarks in the analysis (default is set at equal weight for all benchmarks).</p> <p>For updates of the code please visit our GitHub: https://github.com/octavigomez/Ranking-physics-based-EQ-simulations</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>
Catalogs of Deep Long Period Earthquakes at Klyuchevskoy Volcanic Group (Kamchatka) 2011-2012
Open the record for dataset details and reuse information.
Published and new Geochronology, Thermochronology, Geodetic, and Earthquake Data from the Fairweather Transform Region
<p>Published and new Geochronology, Thermochronology, Geodetic, and Earthquake Data from the Fairweather Transform Region used in <strong>Benowitz, J., </strong>Lease, R., Hauessler, P., Pavlis, P., Mann, M., Fairweather Transform Orogenesis: 25 million years of Crustal-block vertical extrusion and double indenter tectonics since ca. 3 Ma., for <i>Tectonophysics</i>.</p>
Machine-Learning Based Location of the 2021 MW 7.4 Maduo Earthquake Sequence: Insight into Intraplate Seismogenesis
<p>This file is the Machine-Learning Based earthquake catalog of the 2021 MW 7.4 Maduo Earthquake. It is only used for scientific research.</p> <p>ATTENTION!!!</p> <p>The data for the paper "Relocation of the 2024 MS 7.1 Wushi, Xinjiang earthquake sequence and implications for seismogenic structure" is accessible at the website "https://zenodo.org/records/12790377".</p> <p>ATTENTION!!!</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.