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Luangwa Rift Active Fault Database v1.0
<p>First release of the Luangwa Rift Active Fault Database for the submission of a manuscript to EGU Solid Earth.</p> <p>Active fault database for the Luangwa Rift, Zambia compiled by Tess Turner, Luke Wedmore and Juliet Biggs at University of Bristol.</p> <p>The Luangwa Rift Active Fault Database (LRAFD) is a freely available open-source geospatial database of active fault traces within the Luangwa Rift, Zambia.</p> <p>The active fault database has been designed and released in line with the Global Earthquake Model standards. Full details of the criteria used to assess activity will be released in a publication that is currently in preparation.</p> <p><strong>Citation</strong><br> Please cite the latest release of this database on Zenodo in addition to the following manuscript:<br> Turner, T. Wedmore, L.N.J., Biggs, J. Williams, J.N., Sichingabula, H.M., Kabumbu, C., Banda, K. The Luangwa Rift Active Fault Database and fault reactivations along the southwestern branch of the East African Rift. _Submitted to EGU Solid Earth_</p> <p><strong>Data Format</strong><br> The LRAFD is a geospatial database containing a collection of active fault traces in GIS vector format. Each fault is mapped as a single continuous GIS feature, and has associated metadata that describe the geometry of the fault and various aspects of its exposure and the methodology used to map the fault.</p> <p>The list below describes the attributes within the LRAFD. These attributes are based on the <a href="https://github.com/cossatot/gem-global-active-faults">Global Earthquake Model Global Active Faults Database</a> (<a href="https://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a>; <a href="https://doi.org/10.1177%2F8755293020944182">Styron and Pagani, 2020</a>). Note, we do not currently include all attributes from the <a href="http://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a> as these data have not been collected in the Luangwa Rift. It is the intention that future versions of this database will include more attributes. No assessment is made of the seismogenic properties of the faults in the LRAFD as this is subjective. These data have been compiled in the publication associated with this database.</p> <p><br> <strong>Data Table</strong></p> <table> <caption>Luangwa Rift Active Fault Database Attributes</caption> <thead> <tr> <th scope="col">Attribute</th> <th scope="col">Data Type</th> <th scope="col">Description</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>LRAFD_ID</td> <td>integer</td> <td>Unique Fault IDentification number assigned to each fault trace</td> <td> </td> </tr> <tr> <td>Fault_Name</td> <td>string</td> <td>Name of Fault</td> <td>Assigned using local geographic features or towns</td> </tr> <tr> <td>Dip_Direction</td> <td>string</td> <td>Compass quadrant of fault dip direction</td> <td> </td> </tr> <tr> <td>slip_type</td> <td>string</td> <td>kinematic type of fault</td> <td>e.g. normal, reverse, sinistral-strike slip, dextral-strike slip</td> </tr> <tr> <td>Fault_Length</td> <td>decimal</td> <td>Straight line distance between the tips of the fault</td> <td> </td> </tr> <tr> <td>GeomorphicExpression</td> <td>string</td> <td>Geomorphic feature/features used to identify the fault trace and its extent</td> <td>e.g. escarpment, fault scarp, offset sedimentary feature</td> </tr> <tr> <td>Method</td> <td>string</td> <td>DEM or geologic dataset used to identify and map the fault trace</td> <td>e.g. digital elevation model hillshade, slope map</td> </tr> <tr> <td>Confidence</td> <td>integer</td> <td>Confidence of recent (Quaternary) activity</td> <td>Ranges from 1-4, 1 if high certainty, 4 if low certainty</td> </tr> <tr> <td>ExposureQuality</td> <td>integer</td> <td>Fault exposure quality</td> <td>1 if high, 2 if low</td> </tr> <tr> <td>EpistemicQuality</td> <td>integer</td> <td>Certainty of whether a fault exists there</td> <td>1 if high, 2 if low</td> </tr> <tr> <td>Accuracy</td> <td>integer</td> <td>Coarsest scale at which fault trace can be mapped, expressed as the denominator of the map scale</td> <td>reflects the prominence of the fault's geomorphic expression</td> </tr> <tr> <td>GeologicalMapExpression</td> <td>string</td> <td>extent of correlation between fault traces and legacy geological map</td> <td>whether faults have been previously mapped and/or follow geological contacts</td> </tr> <tr> <td>Notes</td> <td>string</td> <td>Any additional or relevant information regarding the fault</td> <td> </td> </tr> <tr> <td>References</td> <td>string</td> <td>Relevant literature/geological maps where the fault is mentioned/described</td> <td> </td> </tr> </tbody> </table> <p> </p> <p><strong>File Formats</strong><br> Following the <a href="http://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a>, this database is provided in a variety of GIS vector file formats. <a href="http://geojson.org/">GeoJSON</a> is the version of record, and any changes should be made in this version, before they are converted to other filed formats using the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database/blob/main/convert.sh">convert.sh</a> shell script available in this repository. This script uses the <a href="https://gdal.org/">GDAL</a> tool <a href="https://gdal.org/programs/ogr2ogr.html">ogr2ogr</a> and is adapted from a script posted by Richard Styron (<a href="https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh">https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh</a>), who we thank for making this publicly available. The other versions available are <a href="https://support.esri.com/en/white-paper/279">ESRI Shapefile</a>, <a href="https://earth.google.com">KML</a>, <a href="https://www.generic-mapping-tools.org/">GMT</a> and <a href="https://www.geopackage.org/">Geopackage</a>.</p> <p>Note that in the <a href="http://support.esri.com/en/white-paper/279">ESRI Shapefile</a> format, the length of the attribute are restricted in length by the format, so we advise against using this format.</p> <p><strong>Version Control</strong><br> This version of the database is v1.0 and is associated with the release of the data for submission of the associated manuscript.</p> <p>It is intended that this database is updated in future versions by both the authors and other users. As such we encourage edits of the [GeoJSON] file and the submission of pull requests on the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database">associated github site</a>. Please contact Luke Wedmore (<<a href="mailto:luke.wedmore@bristol.ac.uk?subject=Luangwa%20Rift%20Active%20Fault%20Databse%20Zenodo%20Release">luke.wedmore@bristol.ac.uk</a>>) for information or to report errors in the database.</p> <p><strong>References</strong><br> Styron, Richard, and Marco Pagani. “The GEM Global Active Faults Database.” Earthquake Spectra, vol. 36, no. 1_suppl, Oct. 2020, pp. 160–180, doi:10.1177/8755293020944182.<br> </p> <p> </p>
Luangwa Rift Fault Scarp Measurements
<p>First Release associated with submission of article to EGU Solid Earth.</p> <p>Scarp height measurements for faults in the Luangwa Rift performed by Tess Turner for her University of Bristol MSci Earth Sciences final year project.</p> <p>If you use these measurements please cite the following paper in addition to this dataset:</p> <p>Turner, T., Wedmore, L.N.J., Biggs, J., Williams, J. Sichingabula, H.M., Kabumbu, C. Banda, K. The Luangwa Rift Active Fault Database and fault reactivation along the southwestern branch of the East African Rift. <em>In preparation for Solid Earth</em></p> <p>These measurements were performed on SRTM data using the method outlined in <a href="https://doi.org/10.1029/2019TC005834">Wedmore et al., 2020</a>. Topographic profiles were sampled event 30 m and stacked at 120 m intervals along strike.</p> <p>Four faults in the Luangwa Rift have been measured using this technique: The Chipola, Chitembo, Kabungo and Molaza faults. The traces of these faults, and all other known active faults in the Luangwa Rift have been separately archived as part of the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database">Luangwa Rift Active Fault Database</a>.</p> <p><strong>Data Format</strong></p> <p>Files are provided in comma separated value (<a href="https://datatracker.ietf.org/doc/html/rfc4180">csv</a>) format. Each file contains one header line with descriptions of the data contained within each column. The column headings and extra information are summarised in the table below. Where data columns #5-10 are blank, this is because no measurements were possible in the profile corresponding to that particular row number. If columns 11-16 are blank, this is because there are no scarp height measurements within the sampling window of the moving average.</p> <p><strong>Attribute Table</strong></p> <table align="left"> <caption>Attribute Table for the measurements of scarp height in the Luangwa Rift</caption> <thead> <tr> <th scope="col">Column #</th> <th scope="col">Attribute</th> <th scope="col">Units</th> <th scope="col">Data Type</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Longitude</td> <td>decimal degrees</td> <td>Float</td> <td> </td> </tr> <tr> <td>2</td> <td>Latitude</td> <td>decimal degrees</td> <td>Float</td> <td> </td> </tr> <tr> <td>3</td> <td>Distance Along Fault</td> <td>kilometers</td> <td>Float</td> <td> </td> </tr> <tr> <td>4</td> <td>Distance Along Fault</td> <td>meters</td> <td>Integer</td> <td> </td> </tr> <tr> <td>5</td> <td>Scarp Height</td> <td>meters</td> <td>Float</td> <td>Mean scarp height measurement of 10,000 iterations of scarp height with varying subset of points in the hanging wall, scarp and footwall slopes.</td> </tr> <tr> <td>6</td> <td>Scarp Height Standard Deviation</td> <td>meters</td> <td>Float</td> <td>Standard deviation of 10,000 iterations of meausuring the scarp height with varying subset of points in the hanging wall, scarp and footwall slopes.</td> </tr> <tr> <td>7</td> <td>Upper Slope Angle </td> <td>degrees</td> <td>Float</td> <td>Mean upper slope dip angle of 10,000 iterations of subset of points selected from the footwall slope (above the top of the fault scarp).</td> </tr> <tr> <td>8</td> <td>upper Slope Angle Standard Deviation</td> <td>degrees</td> <td>Float</td> <td>Standard devation of upper slope angle of 10,000 random subsets of the points selected on the upper slope of the fault.</td> </tr> <tr> <td>9</td> <td>Lower Slope Angle</td> <td>degrees</td> <td>Float</td> <td>Mean lower slope dip angle of 10,000 iterations of subset of points selected from the hanging wall slope.</td> </tr> <tr> <td>10</td> <td>lower Slope Angle Standard Deviation</td> <td>degrees</td> <td>Float</td> <td>Standard devation of lower slope angle of 10,000 random subsets of the points selected on the lower slope of the fault.</td> </tr> <tr> <td>11</td> <td>Filtered median offset (1 km)</td> <td>meters</td> <td>Float</td> <td>median scarp height over 1 km of the distance along strike (0.5 km either side of the point).</td> </tr> <tr> <td>12</td> <td>filtered standard deviation (1km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 1 km of the distance along strike (0.5 km either side of the point).</td> </tr> <tr> <td>13</td> <td>Filtered median offset (3 km) </td> <td>meters</td> <td>Float</td> <td>median scarp height over 3 km of the distance along strike (1.5 km either side of the point).</td> </tr> <tr> <td>14</td> <td>filtered standard deviation (3km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 3 km of the distance along strike (1.5 km either side of the point).</td> </tr> <tr> <td>15</td> <td>Filtered median offset (5 km)</td> <td>meters</td> <td>Float</td> <td>median scarp height over 5 km of the distance along strike (2.5 km either side of the point).</td> </tr> <tr> <td>16</td> <td>filtered standard deviation (5km)</td> <td>meters</td> <td>Float</td> <td>standard deviation scarp height over 5 km of the distance along strike (2.5 km either side of the point).</td> </tr> </tbody> </table> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Version Control</strong><br> This release is archived as part of the publication Turner et al. (submitted to EGU Solid Earth). It is intended that this database will be updated in the future as high resolution topography products become available and/or methods for measuring fault scarps improve. Please contact Luke Wedmore <<a href="mailto:luke.wedmore@bristol.ac.uk?subject=Luangwa%20Rift%20Fault%20Scarp%20Measurements%20Zenodo%20Upload">luke.wedmore@bristol.ac.uk</a>> for more information or if you spot any errors.</p> <p><strong>References</strong><br> Wedmore, L.N.J., Biggs, J., Williams, J.N., Fagereng, Å., Dulanya, Z., Mphepo, F., Mdala, H. (2020) Active Fault Scarps in Southern Malawi and Their Implications for the Distribution of Strain in Incipient Continental Rifts. _Tectonics_, 39(3), e2019TC005834, <a href="https://doi.org/10.1029/2019TC005834">doi.org/10.1029/2019TC005834</a></p>
Relocated Seismicity Catalogs on the Discovery Transform Fault, 4S on the East Pacific Rise
<p>Two relocated earthquake catalogs are provided for the Discovery Transform Fault located at 4ºS on the East Pacific Rise. There is a microseismicity catalog representing one year of activity recorded during a 2008 ocean bottom seismometer deployment, which includes 12,635 events with local magnitudes, M<sub>L</sub>, between 0 and 4.1. The second catalog includes 24 years (1 January 1990 - 1 April 2013) of earthquakes obtained from the global Centroid Moment Tensor (CMT) catalog, a total of 15 events, with seismic moment magnitudes, M<sub>W</sub>, between 5.4 and 6.0.</p> <p>Microseismicity was relocated using the HypoDD relocation algorithm (Waldhauser, 2001), while the CMT events were relocated using a teleseismic surface-wave cross-correlation technique (McGuire, 2008). The 15 CMT events all relocated into one of five distinct rupture patches on the Discovery Transform Fault. In general, microseismicity was found to be reduced within these large, repeating rupture patches.</p> <p>A more detailed description of the methodology used to relocate both catalogs, as well as a discussion on the correlation between seismic behavior and fault structure on the Discovery Transform Fault is provided in:</p> <p>Wolfson-Schwehr, M., Boettcher, M. S., McGuire, J. J., & Collins, J. A. (2014). The relationship between seismicity and fault structure on the Discovery transform fault, East Pacific Rise. <em>Geochemistry, Geophysics, Geosystems, </em>15(9), 3698–3712. <a href="https://doi.org/10.1002/2014GC005445">https://doi.org/10.1002/2014GC005445</a></p> <p>Seismic Catalogs:</p> <ul> <li>Discovery_CMT_relocated_seismicity_1990_2013.csv</li> <li>Discovery_relocated_microseismicity_2008.csv</li> </ul> <p>Additional References:</p> <p>1. McGuire, J. J. (2008). Seismic cycles and earthquake predictability on East Pacific Rise transform faults. <em>Bulletin of the Seismological Society of America</em>, 98(3), 1067-1084. <a href="https://www.whoi.edu/cms/files/McGuire_BSSA_2008_48643.pdf">https://www.whoi.edu/cms/files/McGuire_BSSA_2008_48643.pdf</a></p> <p>2. Waldhauser, F. (2001). hypoDD--A program to compute double-difference hypocenter locations. <br> <a href="https://academiccommons.columbia.edu/doi/10.7916/D8SN072H">https://academiccommons.columbia.edu/doi/10.7916/D8SN072H</a></p>
Earthquake Catalogues for DWARFS (Dense Westland Arrays Researching Fault Segmentation)
<p>This dataset contains earthquake hypocentral information catalogued as part of the DWARFS (Dense Westland Arrays Researching Fault Segmentation) broadband seismometer networks along New Zealand's Alpine Fault, between April 2019-April 2020.</p> <p>'Preferred Lat/Lon/Depth' refers to origin determined by method under 'Method'. HypoDD is the preferred method, but some origins could not be relocated and so we present the NonLinLoc derived origin instead. All magnitudes are Local magnitudes calculated using displacements on the vertical channel (MLv). All times are in UTC time. </p> <p>This dataset accompanies a publication recently submitted (July 2022) to the AGU journal 'Journal of Geophysical Research: Solid Earth' entitled 'Heterogeneity in microseismicity and stress near rupture-limiting section boundaries along the late interseismic Alpine Fault'. </p>
Catalog of microseismicity related to the Alto-Tiberina Fault
<p>This template matching catalog contains information about new detected seismicity as the origin time (ot), latitude (lat_temp), longitude (lon_temp), depth (depth_tep), magnitude (mag), origin time of the template that can be used as the event_id (ot_template), the ratio between the average correlation coefficient (CC) and the daily median absolute deviation of the averaged CCs, as an indication for the quality of a detection (cc_mad_ratio), and an indication about the origin as some of the detection's seem to be related to human induced activity (hum_ind). Detailed information about the template matching processing can be gained from <strong>Spatio-temporal evolution of the</strong><strong> Seismicity in the Alto Tiberina Fault System revealed by a High-Resolution Template Matching Catalog</strong> by Essing & Poli (2022)</p> <p> </p>
Understanding the heterogeneous rheologic structure across the Longmenshan fault from ten-year postseismic GPS observations
<p>The two datasets are the 10-year cumulative displacements following the 2008 Wenchuan earthquake, GPS time-series observations for all sites, GPS time-series simulation for all sites and the secular velocity corresponding to the interseismic tectonic response, respectively.</p>
Forearc faults in northern Cascadia do not accommodate elastic strain driven by the megathrust seismic cycle: Dataset
<p>Input files and codes for Harrichhausen, N., Morell, K.D., Regalla, C. Inner forearc faults in northern Cascadia do not accommodate elastic strain driven by the megathrust seismic cycle. Submitted to Seismica. 2024.</p> <p>See Readme.md for more information</p> <p>Version 1.1: Updated author list and funding information.</p> <p>Version 1.2: Updated matlab codes to work on Mac.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset
<p>Simulated sydromes measurement of quantum surface code error correction.<br>Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>".</p> <p>File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code></p> <p>Each file is formatted as csv with the following columns:</p> <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> <p>Only distance 3 is currently available with 10M samples for each physical fault rate.</p> <p>The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data
<p>Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"</p>
Antarex HPC Fault Dataset
<p>The Antarex dataset contains trace data collected from the homonymous experimental HPC system located at ETH Zurich while it was subjected to fault injection, for the purpose of conducting machine learning-based fault detection studies for HPC systems. Acquiring our own dataset was made necessary by the fact that commercial HPC system operators are very reluctant to share trace data containing information about faults in their systems.</p> <p>In order to acquire data, we executed benchmark applications and at the same time injected faults in the system at specific times via dedicated programs, so as to trigger anomalies in the behaviour of the applications. A wide range of faults is covered in our dataset, from hardware faults, to misconfiguration faults, and finally to performance anomalies cause by interference from other processes. This was achieved through the FINJ fault injection tool, developed by the authors.</p> <p>The dataset contains two types of data: one type of data refers to a series of CSV files, each containing a set of system performance metrics sampled through the LDMS HPC monitoring framework. Another type refers to the log files detailing the status of the system (i.e., currently running benchmark applications or injected fault programs) at each time point in the dataset. Such a structure enables researchers to perform a wide range of studies on the dataset. Moreover, since we collected the dataset by streaming continuous data, any study based on it will easily be reproducible on a real HPC system, in an online way. The dataset is divided in two parts: the first includes only the CPU and memory-related benchmark applications and fault programs, while the second is strictly hard drive-related. We executed each part in both single-core and multi-core variants, resulting in a total of 4 dataset blocks for 32 days of data acquisition, and 20GB of uncompressed data.</p> <p>For a detailed analysis on the structure and features of the Antarex dataset, please refer to the research paper "Online Fault Classification in HPC System through Machine Learning", by Netti et al. Additional details can be found in the research paper "FINJ: a Fault Injection Tool for HPC System" by Netti et al., whereas all source code can be found on the GitHub repository of the FINJ tool.</p> <p>When using this dataset, please cite the two reference papers above as follows:</p> <p>" Netti A., Kiziltan Z., Babaoglu O., Sîrbu A., Bartolini A., Borghesi A. (2019) FINJ: A Fault Injection Tool for HPC Systems. In: Mencagli G. et al. (eds) Euro-Par 2018: Parallel Processing Workshops. Euro-Par 2018. Lecture Notes in Computer Science, vol 11339. Springer, Cham"</p> <p>" Netti A., Kiziltan Z., Babaoglu O., Sîrbu A., Bartolini A., Borghesi A. (2019) Online Fault Classification in HPC Systems through Machine Learning. arXiv:1810.11208"</p>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
3D displacement field and fault-offset measurements for the northern Kaikōura ruptures
<p>Contents:</p> <p>1. East, north and vertical components of the co-seismic displacement field for three faults (the Kekerengu, Jordan and Upper Kowhai faults) that ruptured in the 2016 Kaikoura earthquake, New Zealand (east.tif, north.tif, vertical.tif).</p> <p>2. Shapefiles containing offsets across the faults of interest, measured from the displacement field (shapefiles.zip).</p> <p>3. CSV files containing offsets across the faults of interest, measured from the displacement field (csvs.zip). </p> <p>Our methodology is described in the following manuscript:</p> <p>Howell et al., 2019. 3D surface displacements during the 2016 MW 7.8 Kaikōura earthquake (New Zealand) from photogrammetry-derived point clouds, Journal of Geophysical Research Solid Earth, submitted.</p>
SCEC Community Fault Model (CFM)
<h1>Introduction</h1> <p>The Statewide California Earthquake Center (SCEC) Community Fault Model (CFM) is an object-oriented, fully three-dimensional geometric representation of active faults in California and adjacent offshore basins. For each fault object, the CFM provides triangulated surface representations (t-surfs) in several resolutions, fault traces in several different file formats (shape files, GMT plain text, and GoogleEarth kml), and complete metadata including references used to constrain the surfaces. The CFM faults are defined based on available data including surface traces, seismicity, seismic reflection profiles, well data, geologic cross sections, and various other types of data and models. The CFM serves SCEC as a unified resource for physics-based fault systems modeling, strong ground-motion prediction, probabilistic seismic hazards assessment (e.g., the USGS National Seismic Hazard Model), and many other uses. Together with the Community Velocity Model (CVM-H 15.1.0), the CFM comprises SCEC's Unified Structural Representation of the Southern California crust and upper mantle (Shaw et al., 2015).</p> <h1>Current Model Version: CFM 7.0</h1> <p>The current version of the SCEC CFM is version 7.0 (CFM 7.0), which builds on the previous CFM releases and serves as the latest update to Plesch et al. (2007). CFM 7.0 is a significant update as this is the first CFM to cover the entire state of California, spanning the Pacific-North American plate boundary from northern Mexico to the southern Cascadia subduction zone. This latest version has no changes to the southern California portion of the model, but now includes 113 new fault representations in central and northern California in the preferred model. These new central and northern California fault representations will undergo a community evaluation in 2024-2025, therefore, the central and northern California faults should be considered preliminary representations.</p> <p>CFM 7.0 contains three fully-documented sub models: preferred, ruptures, and alternatives. In total, CFM 7.0 comprises the following components: </p> <ol> <li> <p><strong>CFM 7.0 Preferred</strong>: A set of 556 fault objects that constitute the preferred set of active faults. These faults have attained preferred status based on past community evaluations or are new representations.</p> </li> <li> <p><strong>CFM 7.0 Ruptures</strong>: A set of 13 fault objects assembled from the CFM 7.0 preferred model that ruptured during selected significant historic events. These are not earthquake source models, but are representations of the entire fault surfaces where a significant historic rupture occurred. This model is intended to indicate which CFM fault objects were involved with selected significant historic ruptures.</p> </li> <li> <p><strong>CFM 7.0 Alternatives</strong>: A set of 39 alternative representations where structural differences have been proposed that could potentially significantly impact fault mechanics and associated seismic hazards. These alternative representations were selected based on community rankings following a comprehensive evaluation of the CFM that took place in May of 2022.</p> </li> </ol> <p>Including all sub models, the CFM 7.0 incorporates 608 fully-documented fault objects. If you use the CFM, we would appreciate you citing both Plesch et al. (2007) and the DOI where the archive is stored.</p> <h1>Directory Structure and Contents of the CFM Archive</h1> <p>The CFM archive directory structure is as follows:</p> <p><strong>doc/</strong><br>Documentation and metadata, which include an MS Excel spreadsheet with detailed metadata about each fault surface. Metadata for the preferred, rupture, and alternative models are provided in separate but otherwise identically formatted sheets within the file. All faults contain references to the works that helped to define the 3D fault surface geometry. More information about the metadata columns is provided in doc/README.txt</p> <p><strong>obj/preferred/</strong><br><strong>obj/ruptures/</strong><br><strong>obj/alternatives/</strong><br>These directories contain the model components for the preferred, rupture, and alternative models, respectively. Each model contains an identical directory structure, which is described below using the preferred model as an example.</p> <p><strong>obj/preferred/native/</strong><br>The CFM preferred fault surfaces in gocad tsurf format using the native mesh. The native mesh uses a variable mesh resolution. Smaller triangles generally indicate where a fault is well-constrained by data. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/500m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~500m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/1000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~1000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/2000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~2000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/traces/</strong><br>Fault traces and upper tip lines (for blind faults) of the CFM preferred faults. While the CFM is a 3D model, it is often useful to make map-based visualizations of the model. The traces and blind faults are provided in several different formats described below.</p> <p><strong>obj/preferred/traces/gmt/<br></strong>Fault traces and blind faults in Generic Mapping Tools multiple segment file ASCII format (i.e., plain text).<br> .lonLat - Longitude/Latitude coordinates (WGS84 datum)<br> .utm - UTM zone 11 NAD27 datum (EPSG:26711)</p> <p><strong>obj/preferred/traces/kml/</strong><br>Fault traces and blind faults in Google Earth .kml format (WGS84 datum). The kml files also contain selected metadata as attributes which can be imported into QGIS. When a fault trace is clicked on in the Google Earth interface, a mini-webpage with metadata information will pop up.</p> <p><strong>obj/preferred/traces/shp/</strong><br>Fault traces and blind faults in GIS shapefile format (longitude/latitude coordinates, WGS84 datum).</p> <h1>CFM Contributors</h1> <p>The current and past versions of the CFM would not be possible without contributions from numerous SCEC community members. We would like to thank the following CFM contributors:</p> <p>Christine Benson, <a href="https://central.scec.org/user/bbryant">William Bryant</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dolan">James Dolan</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, Russell Graymer, <a href="https://central.scec.org/user/jhubbard">Judith Hubbard</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, Sam Johnson, <a href="https://central.scec.org/user/ylevy">Yuval Levy</a>, <a href="https://central.scec.org/user/lgrant">Lisa Grant Ludwig</a>, <a href="https://central.scec.org/user/hauksson">Egill Hauksson</a>, <a href="https://central.scec.org/user/tjordan">Thomas Jordan</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, Keith Knudsen, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/lindvall">Scott Lindvall</a>, <a href="https://central.scec.org/user/harold">Harold Magistrale</a>, James Lienkaemper, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/niemi">Nathan Niemi</a>, Stu Nishenko, <a href="https://central.scec.org/user/oskin">Michael Oskin</a>, <a href="https://central.scec.org/user/perry">Sue Perry</a>, <a href="https://central.scec.org/user/planansky">George Planansky</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/rockwell">Thomas Rockwell</a>, David Schwartz, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/pshearer">Peter Shearer</a>, Bob Simpson, <a href="https://central.scec.org/user/sorlien">Christopher Sorlien</a>, M. Peter Süss, <a href="https://central.scec.org/user/suppe">John Suppe</a>, <a href="https://central.scec.org/user/treiman">Jerry Treiman</a>, Jeff Unruh, Janet Watt, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, Chris Wills, <a href="https://central.scec.org/user/yeats">Robert Yeats</a>, and every colleague that has participated in a CFM community evaluation. We could not make the CFM without this community effort.</p> <h1>CFM Evaluators</h1> <p>Before assembling CFM 6.0 and subsequently CFM 7.0, a team of SCEC colleagues participated in a rigorous evaluation of CFM 5.3 in April-May of 2022. This evaluation was open to the SCEC community and focused on 23 critical fault representations where different proposed interpretations have the potential to significantly affect seismic hazards. This evaluation resulted in 14 new fault representations in the CFM 6.0 preferred model. The lower ranked representations are now provided in the CFM alternatives. We would like to thank the following CFM evaluators for volunteering their time and expertise to this process:</p> <p><a href="https://central.scec.org/user/sakciz">Sinan Akçiz</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dawson">Tim Dawson</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/ajelliott">Austin Elliot</a>, <a href="https://central.scec.org/user/frost">Erik Frost</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/aganas">Athanassios Ganas</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, <a href="https://central.scec.org/user/alexhatem">Alex Hatem</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, <a href="https://central.scec.org/user/christos">Christodoulos Kyriakopoulos</a>, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/kluttrell">Karen Luttrell</a>, <a href="https://central.scec.org/user/madden">Chris Madugo</a>, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/meigsa">Andrew Meigs</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/nonderdo">Nate Onderdonk</a>, <a href="https://central.scec.org/user/absrp">Alba Rodríguez Padilla</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/scharer">Kate Scharer</a>, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/sorlien">Chris Sorlien</a>, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, <a href="https://central.scec.org/user/yule">Doug Yule</a>, <a href="https://central.scec.org/user/jzachariasen">Judy Zachariasen</a>.</p> <p> </p>
Dataset for Rate and Pressure Dependence of Dilatancy and Fault Strength in Partially-Drained Laboratory Fault Zones
<p>The datasets for Affinito et al., 2024 manuscript. Each experiment is was collected on a 24-bit recorder and 16 channels for hydromechanical data. The purpose of these experiments was to document the transition in fault drainage state as a fuction of shearing rate. Samples were prepared from cores collected at the US-DOE Utah FORGE well 16A.</p>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
HR-GNSS data used in Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
<p>Here are the high-rate GNSS data we used to infer the low-frequency components of seismic source radiation within the M 6.2, 24/August/2016, Amatrice Earthquake. In particular, the traces are used to constrain frequencies between 0.03-0.06 Hz. This data has been used to evaluate the performance of the method, in a train/test split procedure, described in the manuscript. We upload data here to comply with AGU Fair data policy (https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Policies/Data-policy)</p> <p>Please find the pre-print of the manuscript from the ESSOAR (<a href="https://doi.org/10.1002/essoar.10504341.1">https://doi.org/10.1002/essoar.10504341.1</a>).</p> <p>Notice that the complete set of data are reposited on INGV FTP server: ftp://gpsfree.gm.ingv.it/amatrice2016/</p> <p>The data is originally processed by Avallone et al. (2016), and the detailed analysis procedure has been explained there. In the case where you used this data, please cite the original articles: </p> <p>Avallone, A., Latorre, D., Serpelloni, E., Cavaliere, A., Herrero, A., Cecere, G., ... & Selvaggi, G. (2016). Coseismic displacement waveforms for the 2016 August 24 Mw 6.0 Amatrice earthquake (central Italy) carried out from High-Rate GPS data. Annals of Geophysics, 59. (<a href="https://doi.org/10.4401/ag-7275">https://doi.org/10.4401/ag-7275</a>)</p> <p>Avallone, A., Selvaggi, G., D'Anastasio, E., D'Agostino, N., Pietrantonio, G., Riguzzi, F., ... & Zarrilli, L. (2010). The RING network: improvement of a GPS velocity field in the central Mediterranean. Annals of Geophysics, 53(2), 39-54. (<a href="https://doi.org/10.4401/ag-4549">https://doi.org/10.4401/ag-4549</a>)</p> <p> </p>
Data for paper "Parametric analyses of attack-fault trees"
<p>This is the dataset for paper "Parametric analyses of attack-fault trees" published in the proceedings of the 19th International Conference on Application of Concurrency to System Design (ACSD 2019).</p>
The Interseismic Seismicity of the East Anatolian Fault Between 2007-2012 and Aftershock Locations of the 2020 Mw6.8 Sivrice Earthquake
<p>The two files include the seismicity along the Eastern Anatolian Fault In Turkey between 2007 and 2012 and the Aftershocks of the January 24, 2020 Mw6.8 Sivrice (Elazığ) earthquake.</p>
ICP Displacement Fields from the 2016 Mw 7.8 Kaikōura Earthquake over the Papatea Fault, New Zealand
<p>Three-dimensional displacement fields produced over the Papatea Fault, South Island, New Zealand following the 2016 Mw 7.8 Kaikōura earthquake. The dataset was generated from pre- and post-event aerial image point clouds using a windowed implementation of the iterative closest point algorithm.</p> <p>Creation Date: 8/28/2021</p> <p>Authors: Colin Bloom (University of Canterbury, Christchurch, New Zealand), Tim Stahl (University of Canterbury), and Andy Howell (University of Canterbury/GNS Science, Lower Hutt, New Zealand)</p> <p>Projection: New Zealand Transverse Mercator</p> <p>Scale: 25 m/pixel</p> <p>Notes: There are three displacement directions, east, north, and vertical. Positive values represent east, north, and up in the vertical direction respectively in relation to the pre-event surface. Displacement values are in meters. Extremely high or low data values likely represent noise in the dataset.</p>
Resarch data for common faults tested on a variable-speed propane-charged heat pump on heating mode
<p>Experimental data of common faults emulated on a 10 kW water-to-water variable-speed heat pump charged with propane. The faults emulated are evaporator fouling, compressor valve leakage, liquid line restriction and refrigerant overcharge. The faults are tested with 10 kW and 12 kW load demand.</p> <p>This data can be used to develop fault detection and diagnosis systems.</p>
ScienceDex guides
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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.