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781 results for “earthquakes”
Data for "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript
<p>Data necessary to support the analysis presented in "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript</p>
Relationship between rupture length and magnitude of oceanic transform fault earthquakes
<p>We provide here the supplementary material to <em><strong>Relationship between rupture lengths and magnitudes of oceanic transform fault earthquakes,</strong> </em>by Guilherme de Melo, Ingo Grevemeyer, Dietrich Lange, Dirk Metz, and Heidrun Kopp.</p> <p> </p>
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
InSAR coseismic deformation for the 22 January 2024, Mw 7.0, Wushi (northwestern China) earthquake
<p>This dataset includes coseismic InSAR deformation for the 2024 Mw 7.0 Wushi (northwestern China) earthquake, slip models as well as Coulomb stress change distributions.</p>
Large megathrust earthquakes tend to sustain an increasingly longer duration than expected
<p>This is the data used for the paper "Large megathrust earthquakes tend to sustain an increasingly longer duration than expected".</p>
Potency Magnitude Catalog for Western US Earthquakes: 1950-2024
<p>Earthquake catalog for the western US, derived from the US Geological Survey's Comprehensive Catalog (<a href="https://earthquake.usgs.gov/earthquakes/search/" target="_blank" rel="noopener">https://earthquake.usgs.gov/earthquakes/search/</a>) and supplemented with estimates of seismic potency and potency magnitude. The dataset is space-delimited with the following columns, listed on the header line:</p> <ul> <li>evid: ComCat event id</li> <li>time: ComCat preferred origin time (UTC)</li> <li>lat: ComCat preferred latitude</li> <li>lon: ComCat preferred longitude</li> <li>dep: ComCat preferred event depth</li> <li>mag: ComCat preferred magnitude</li> <li>mag_typ: ComCat preferred magnitude type</li> <li>logP0: Estimate of seismic potency in units of cm*km^2 (log10)</li> <li>Mp: Potency magnitude, equivalent to moment magnitude (Mw) assuming a shear modulus of 36 GPa in calculated moment</li> <li>main: Clustering designation (1 or 0) indicating whether or not the event is the mainshock within a cluster of events. Clustering is defined via the nearest-neighbor diagram method of Zaliapin and Ben-Zion (2013).</li> </ul> <p>If you use this dataset in your research, please cite: </p> <div> <div>Trugman, D. T., & Ben‐Zion, Y. (2024). Potency–Magnitude Scaling Relations and a Unified Earthquake Catalog for the Western United States. <em>The Seismic Record</em>, <em>4</em>(3), 223–230. <a href="https://doi.org/10.1785/0320240022">https://doi.org/10.1785/0320240022</a></div> </div>
Input and Output Data for local earthquake tomography in the central Dead Sea Fault using PyVoroTomo
<p>Data to reproduce wave velocity models for the central DSF.</p> <p>eventsAndArrivals.h5 - 2 csv files (keys: events, arrivals)</p> <p>stations_sub.h5 - csv file containing station data</p> <p>gitter.csv - 1D velocity model by Gitterman et al. (2002)</p> <p>3d_Vp_Vs_VpVs_models.nc - velocity models for vp, vs, and vp/vs and their uncertainty.</p> <p>relocated seismicity.h5 - relocated seismicity, arrivals used, and stations used (keys: events, arrivals, stations)</p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Sichuan GNSS data and earthquake catalog
<p>The GNSS data is provided by GNSS data product service platform of China Earthquake Administration. </p>
Active structural geometries and their correlation with moderate (M 5.5-7.0) earthquakes in the Jiashi-Keping region, Tian Shan southwestern front
<p>This data set contains Figures S1-S5, which serve as supporting information for the paper titled "Active structural geometries and their correlation with moderate (M 5.5-7.0) earthquakes in the Jiashi-Keping region, Tian Shan southwestern front". Figure S1 shows locations of all seismic-reflection profiles used in this study. Figures S2-S5 provide uninterpreted and interpreted seismic-reflection profiles across the Bachu transpressional fault system, the western Keping Thrust and the blind Bashituopu Thrust.</p>
Supporting Data for pyCSEP: A Software Toolkit for Earthquake Forecast Developers
<p>Contains data needed to reproduce the figures from the publication of pyCSEP: A Software Toolkit for Earthquake Forecast Developers.</p> <p><br> evaluation_catalog.json<br> evaluation_catalog_zechar2013_merge.txt<br> SRL_2018031_esupp_Table_S1.txt<br> <br> bird_liu.neokinema-fromXML.dat<br> ebel.aftershock.corrected-fromXML.dat<br> helmstetter_et_al.hkj.aftershock-fromXML.dat<br> lombardi.DBM.italy.5yr.2010-01-01.dat<br> meletti.MPS04.italy.5yr.2010-01-01.dat<br> werner.HiResSmoSeis-m1.italy.5yr.2010-01-01.dat<br> <br> config.json<br> m71_event.json<br> results_complete.bin</p>
Data for: Parallel recolonisations generate distinct genomic sectors in kelp following high magnitude earthquake disturbance
<p>Large-scale disturbance events have the potential to drastically reshape biodiversity patterns. Notably, newly vacant habitat space cleared by disturbance can be colonised by multiple lineages, which can lead to the evolution of distinct spatial 'sectors' of genetic diversity within a species. We test for disturbance-driven sectoring of genetic diversity in intertidal southern bull kelp, <i>Durvillaea antarctica</i> (Chamisso) Hariot following the high-magnitude 1855 Wairarapa earthquake in New Zealand. Specifically, we use genotyping-by-sequencing (GBS) to analyse fine-scale population structure across the uplift zone to assess the fit of alternative recolonisaton models. Our analysis reveals that specimens from the uplift zone carry genomic signatures distinct from populations in other regions, consistent with recolonisation after the 1855 earthquake. Crucially, our analysis identifies two parapatric spatial-genomic sectors of <i>D. antarctica</i> at Turakirae Head, which experienced the most dramatic uplift. We infer that bull kelp in the Wellington region survived moderate uplift and recolonised the devastated Turakirae Head coastline through two parallel, eastward recolonisation events. By identifying multiple parapatric genotypic sectors within a recently recolonised coastal region, the current study confirms that competing lineage expansions can generate striking spatial structuring of genetic diversity, even in highly dispersive taxa.</p>
Self-Limiting Earthquake Dynamics and Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity
<p>Earthquakes are among nature’s deadliest and costliest hazards. Physics-based simulations are essential for overcoming the lack of data and elucidating the complex patterns of earthquakes. Enabled by a novel numerical scheme, this work discovers a new mechanism for regulating earthquake dynamics that emerges due to the co-evolution of fault slip and fault zone plasticity. It enables transition from periodic events to fully irregular sequences of earthquakes. The impact of plasticity on earthquake source characteristics goes beyond its limited contribution to the overall energy budget, emphasized in earlier studies, to underscore its crucial role on the redistribution of stresses that self-limits earthquake growth and leads to clustering of seismicity. This work highlights the need for characterizing the fault zone mechanical response beyond their elastic properties to better inform seismic hazard models.</p>
Dataset for "The paucity of supershear earthquakes on large faults governed by rate and state friction"
<p>This is the dataset for the preprint "The paucity of supershear earthquakes on large faults governed by rate and state friction" including the source code and all the input files used for generating the numerical results.</p>
Relocated earthquakes along the Reeves-Pecos County Line in the Delaware Basin
<p>The files contain the relocated earthquakes in the study "On the Depth of Earthquakes in the Delaware Basin – A Case Study along the Reeves-Pecos County Line" by Yixiao Sheng, Karissa S. Pepin and William L. Ellsworth. The manuscript has been submitted to <em>The Seismic Record</em>. </p>
Spatiotemporal variation of the 2010 Yushu Mw 6.9 earthquake sequence: Insight from the 3-D electrical resistivity structure
<p>This dada set will be available in GRL, it was support by the Chinese Earthquake Administration, and it was also supported by the National Natural Science Foundation of China (Grant No. 41674081).</p>
SeisSol input files for the dynamic rupture scenarios based on the 2004 Sumatra-Andaman earthquake published in Madden et al. (2022) "The state of pore fluid pressure and 3D megathrust earthquake dynamics" JGR-Solid Earth
<p>This dataset contains the input files of the dynamic rupture scenarios from Madden, E. H., T. Ulrich and A.-A. Gabriel (2022), The State of Pore Fluid Pressure and 3-D Megathrust Earthquake Dynamics, Journal of Geophysical Research-Solid Earth, <a href="https://doi.org/10.1029/2021JB023382">https://doi.org/10.1029/2021JB023382</a>. (Earlier preprint available at: <a href="https://doi.org/10.1002/essoar.10508297.1">https://doi.org/10.1002/essoar.10508297.2</a>)</p> <p><strong>easi/yaml parameter files for the 6 scenarios studied: </strong><br> PAR_Sumatra_scen1new_gen.par, PAR_Sumatra_scen2new_gen.par, PAR_Sumatra_scen3new_gen.par, PAR_Sumatra_scen4new_gen.par, PAR_Sumatra_scen5new_gen.par, PAR_Sumatra_scen6new_gen.par</p> <p><strong>easi/yaml files setting initial on-fault friction, stress and pore fluid pressure conditions for the 6 scenarios studied: </strong>iniStress_Sumatra_scen1new.yaml, iniStress_Sumatra_scen2new.yaml, iniStress_Sumatra_scen3new.yaml, iniStress_Sumatra_scen4new.yaml, iniStress_Sumatra_scen5new.yaml, iniStress_Sumatra_scen6new.yaml<br> <br> <strong>easi/yaml file describing the rock elastic properties in all 6 scenarios:</strong> <br> matprops_Sumatra_2019_LVZ.yaml<br> <br> <strong>mesh file:</strong> <br> topo4_splays_fix9-14.1e6-28m.dtc1-v2-suma</p> <p> </p>
The seismic data of the 2020 Nima earthquake obatined by USGS, CENC and GCMT
<p>The three files contain the seismic data of the 2020 Nima earthquake obtaind by United States Geological Survey, China Earthquake Data Center and Global Centroid-Moment-Tensor (CMT) Project.</p> <p>If you have any questions about this data, please contact me via <a href="mailto:gaohuastudent@163.com">gaohuastudent@163.com</a>.</p>
Line-Source Model based Rapid Inversion for Deriving Large Earthquake Rupture Characteristics using High-rate GNSS Observations
<p>The high-rate GNSS data and GNSS-derived velocity waveforms of six large earthquakes (the 2016 Mw 6.6 Norcia earthquake, the 2010 Mw 7.2 EI Mayor-Cucapah earthquake, the 2016 Mw 7.8 Kaikoura earthquake, the 2019 Mw 7.1 Ridgecrest earthquake, the 2014 Mw 8.2 Iquique earthquake, and the 2015 Mw 8.3 Illapel earthquake) are included in this repository.</p>
SAR displacements of the 2022 Menyuan earthquake, China
<p>The dataset includes the SAR displacement observations, three-dimensional displacements, and the strain invariants related to the 2022 Menyuan earthquake, China.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.