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562 results for “Faults”
Red Wood-Ant Nests and Fault-Related Methane Micro-Seepage 2016
We measured methane (CH4) and stable carbon isotope of methane (ẟ13C-CH4) concentrations in ambient air and within a red wood-ant (RWA; Formica polyctena) nest in the Neuwied Basin (Germany) using high-resolution in-situ sampling to detect microbial, thermogenic, and abiotic fault-related micro-seepage of CH4. Methane degassing from RWA nests was not synchronized with earth tides, nor was it influenced by micro-earthquake degassing or concomitantly measured RWA activity. Two ẟ13C-CH4 signatures were identified in nest gas: −69‰ and −37‰. The lower peak was attributed to microbial decomposition of organic matter within the RWA nest, in line with previous observations that RWA nests are hot-spots of microbial CH4. The higher peak has not been reported in previous studies. We attribute this peak to fault-related CH4 emissions moving via fault networks into the RWA nest, which could originate either from thermogenic or abiotic CH4 formation. Sources of these micro-seepages could be Devonian schists, iron-bearing “Klerf Schichten,” or overlapping micro-seepage of magmatic CH4 from the Eifel plume. Given the abundance of RWA nests on the landscape, their role as sources of microbial CH4 and biological indicators for abiotically-derived CH4 should be included in estimation of methane emissions that are contributing to climatic change.
Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)
<p>Supplementary material for Sawi et al., 2023, <i>Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms </i>(The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. </p><p> </p><p><strong>Catalog Header</strong></p><p>YR/MO/DY...........Date of event</p><p>HR/MN/SC...........Time of event</p><p>LAT/LON/DEP........Location of event</p><p>EX/EY/EZ...........Relative location uncertainty (in m)</p><p>MAG................NCSN magnitude</p><p>evID.................NCSN event ID</p><p>seqID................Repeating earthquake sequence ID</p><p>isRESp............Is quasi-periodic RES (bool)</p><p> </p><p><strong>References: </strong></p><p>Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. </p><p>Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. <a href="https://doi.org/10.1029/2021JB022495">https://doi.org/10.1029/2021JB022495</a></p>
Data for: Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)
<p>This dataset is associated to the article "Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)" published in Tectonics (<a href="https://doi.org/10.1029/2021TC006870">https://doi.org/10.1029/2021TC006870</a>).</p> <p>It includes the following:</p> <ul> <li>A description file, including a list of data files, and a description of how the marker quality score was determined in this study ("Supporting Information.docx")</li> <li>A table summarizing the historical earthquakes in the region of interest ("TableS1.xlsx")</li> <li>A table summarizing the paleoseismic investigations in the region of interest ("TableS2.xlsx")</li> <li>The full horizontal offset retrodeformations ("offsets_X.tif")</li> <li>A table of the offset values measured along the MNAF (TableS3.xlsx")</li> <li>The georeferenced fault map ("MNAF_2021.gml" and "MNAF_2021.xsd")</li> <li>A figure showing examples of vertical slip markers along the MNAF south of Iznik Lake ("FigS1.tif")</li> <li>A figure showing field examples of Late Quaternary faulting along the MNAF ("FigS2.png")</li> <li>A figure showing the results of the automatic fault discretization procedure ("FigS3.png")</li> </ul>
Fault scarp and structural measurements along the Thyolo Fault, southern Malawi
<p>Measurements of fault scarp height, topographic profiles used to measure fault scarp and metamorphic foliation measurements along the Thyolo Fualt, southern Malawi.</p> <p>This dataset is used in Wedmore, LNJ, Williams, JN, Biggs, J, Fagereng, Å, Mphepo, F, Dulanya, Z, Willoughby, J, Mdala, H, Adams, BA. 2020. Structural inheritance and border fault reactivation during active early-stage rifting along the Thyolo fault Malawi. <em>Journal of Structural Geology</em>, 139, 104097. <a href="https://doi.org/10.1016/j.jsg.2020.104097">https://doi.org/10.1016/j.jsg.2020.104097</a></p> <p>For more information please contact luke.wedmore@bristol.ac.uk</p>
Fault Analysis Database with Features (FADbF)
<p>This repository is also available in GitHub: <a href="https://github.com/leandroensina/FADbF">https://github.com/leandroensina/FADbF</a></p><p>The FADbF dataset companions the paper entitled "Fault Distance Estimation for Transmission Lines with Dynamic Regressor Selection", published in <i>Neural Computing and Applications</i>, <strong>doi</strong>: <a href="https://doi.org/10.1007/s00521-023-09155-y">10.1007/s00521-023-09155-y</a>. More information about the dataset can be found in this reference.</p><p><strong>Associated Tasks</strong>: classification and regression</p><p><strong>Instances</strong>: 168,000</p><p><strong>Attributes</strong>: 128, including the two possible targets</p><p><strong>Additional Information</strong>: this database comprises several attributes extracted from time series of fault simulations of a transmission line with 500 kV, 414 km, and 60 Hz. In total, we extracted 21 features separately for each of the three phases for both voltage and current waveforms along two post-fault cycles from a single terminal, resulting in 126 attributes (21 * 3 * 2 = 126) in addition to the two possible targets, i.e., fault type (classification task) and fault location (regression task). If desired, the fault type can also be used as a feature for the fault location task.</p>
Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
<p><strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong></p> <p>Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.</p> <p> </p> <p><strong>Change log for v1.0.1:</strong></p> <p>Fix wrong Bug ID for <em>sqlite</em> and add README clarification.</p> <p><strong>Change log for v1.0.2:</strong></p> <p>Added an example linking data in the repository to the table.</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
High-precision Aftershock Locations and Fault Planes of the 2016-2017 Central Italy Sequence
<p>The earthquake catalog includes high-precision hypocenter relocations for 390,334<br> earthquakes recorded during the 2016-2017 Amatrice (Central Italy) <br> earthquake sequence. The relative locations were computed by double-difference inversion of a <br> combination of INGV phase picks and cross-correlation differential <br> times measured from correlated seismograms with correlation coefficients > 0.7.</p> <p>Planes of normal faults (idx=1-5) are derived from PCA analysis of 2 months of aftershock <br> locations in the CAT4 catalog following large events. Surfaces of detachment faults (idx=7-10) are derived from mapping out the location of correlated earthquakes. </p> <p>Citation: Waldhauser, F., Michele, M., Chiaraluce, L., Di Stefano, R., & Schaff, D. P. (2021). Fault planes, fault zone structure and detachment fragmentation resolved with highprecision aftershock locations of the 2016-2017 central Italy sequence. Geophysical Research Letters, 48, e2021GL092918. https://doi.org/10.1029/2021GL092918</p>
Third Uniform California Earthquake Rupture Forecast (UCERF3) Fault System Solutions
<p>Data files for the Third Uniform California Earthquake Rupture Forecast (UCERF3), as described in <a href="https://doi.org/10.1785/0120130164">https://doi.org/10.1785/0120130164</a>.<br> <br> These data are stored in the original UCERF3 Fault System Solution file format, which uses binary files within zip containers. This format is being revised, and updates to this dataset will be published when the new and more user friendly format is finalized. See <a href="https://opensha.org/File-Formats">https://opensha.org/File-Formats</a> for more information.<br> <br> File descriptions:<br> <br> <strong>Branch Averaged Files</strong></p> <p>These files contain branch-averaged fault system solutions, where rupture properties (magnitude, rake, rate of occurrence, etc) are averaged across all UCERF3 logic tree branches, according to each branch's weighting in the final model. This is the simplest version of the model, and can be used as a quick approximation to mean hazard. One file exists for each fault model, and these files are compatible with the time-dependent version of UCERF3.</p> <ul> <li><em>branch_averaged_ucerf3_sol_FM3_1.zip</em> - fault model 3.1 branch averaged fault system solution</li> <li><em>branch_averaged_ucerf3_sol_FM3_2.zip</em> - fault model 3.2 branch averaged fault system solution</li> </ul> <p><strong>Full Model (Compound Solutions)</strong></p> <p>These files contain the full UCERF3 logic tree, and can be used to extract data for individual logic tree branches (e.g., for use in hazard calculations that consider all epistemic uncertainties).</p> <ul> <li><em>full_ucerf3_compound_sol.zip</em> - full compound solution file with information on all 1,440 time-independent logic tree branches</li> <li><em>full_ucerf3_compound_sol_with_individual_runs.zip</em> - same as above, but also containing rates for each of 10 simulated annealing inversion runs for each logic tree branch (total of 14,400 inversions)</li> </ul> <p><strong>True Mean Solutions</strong></p> <p>A different type of branch averaged solution, the “true mean” solution, is also available. They are similar to the branch averaged fault system solution described above, but instead use duplicate versions of each rupture whenever a key property (rake, magnitude, area) changes. This retains all variability allowing for quick reproduction of mean UCERF3 results with a minimum set of ruptures. The MeanUCERF3 ERF implemented in <a href="https://opensha.org">OpenSHA</a> uses these files and also allows the user to apply various approximations to further reduce the rupture count.</p> <p>Note: These solutions are not compatible with time dependent UCERF3 calculations as multiple instances of each subsection may exist, resulting in rate partitioning between instances and incorrect recurrence intervals for renewal model calculations.</p> <ul> <li><em>true_mean_ucerf3_sol.zip</em> - true mean fault system solution, across both fault models</li> <li><em>true_mean_ucerf3_sol_FM3_1.zip</em> - true mean fault system solution, only for fault model 3.1</li> <li><em>true_mean_ucerf3_sol_FM3_2.zip</em> - true mean fault system solution, only for fault model 3.2</li> </ul> <p><strong>Metadata</strong></p> <p>A copy of the original file format description is included in <em>file_format.md</em>, and is also <a href="https://opensha.org/File-Formats">available online here</a>. A CSV file that includes information on each gridded seismicity location is also included (<em>relm_gridded_region.csv</em>).</p>
Fault-Tollerant Converter experimental data
<p>The set of files contain the experimental dataset from the tests performed to the Fault-Tolerant Power Converter.</p> <p>Files contain:</p> <ul> <li>“Fault-Tolerant Converter experimental data [Dataset Description].pdf”: Dataset description.</li> <li>“Test_Sxxx.mat”: <em>.mat files (from Matlab) with the experimental data of each of the failure tested</em>/emulated.</li> <li>“Test Sxxx preview.png”: Screenshots of the several Graphs obtained with the .mat data set.</li> </ul> <p>The dataset was provided by the Power Systems Group of the Electrical Engineering Research Area, IREC (Spain).</p>
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
Dataset for "Partitioned fault movement and aftershock triggering: evidence for fault interactions during the 2017 Mw 5.4 Pohang earthquake, South Korea"
<p>This repository contains the seismograms of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and the Korea Institute of Nuclear Safety (KINS) used in Son et al. (2020). The uploaded waveforms were filtered according to the Supporting Information of Son et al. (2020). Continuous waveforms are available via the Korea Meteorological Administration (KMA; http://necis.kma.go.kr).</p> <p>Suggested citation: Son, M., Cho, C. S., Lee, H. K., Han, M., Shin, J. S., Kim, K., Kim, S. (2020). Partitioned fault movement and aftershock triggering: evidence for fault interactions during the 2017 Mw 5.4 Pohang earthquake, South Korea. Journal of Geophysical Research: Solid Earth, e2020JB020005. <a href="https://doi.org/10.1029/2020JB020005">https://doi.org/10.1029/2020JB020005</a></p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities
<p>Numerical data used to make Figures in the manuscript entitled "Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities". We provide the data to create Figures 1 to 4 from the main text and Figures S1 to S9 from the supplementary information. We also provide Python scripts to plot them.</p>
Fracture Data Supporting: 'The 2024 Mw4.8 New Jersey Intraplate Earthquake: Preferential Rupture of an Immature Fault in Frictionally Unstable Basement Rocks'
<p>The spreadsheets contain fracture and paleoslip surface datasets measured across the epicentral region of the April 5, 2024 Mw4.8 New Jersey earthquake. Datasets contain coordinates of outcrops, strike, dip, and trend/plunge or rake (where slickenlines are observed).</p>
Fracture Caging in a Porous Lab Fault: Complementary Data
<p>This dataset is complementary experiment data to the already published dataset (doi.org/10.5281/zenodo.10951458) related to fracture caging in shear. It includes the viscosity and flow rate variables in fracture caging study in a porous lab fault. The ReadMeFirst.txt file contains the necessary information to understand the data structure. </p> <p>Three more experiments are added to this complementary experiment dataset. Details are provided. </p>
Effects of Periodic Normal Stress Oscillations on Frictional Properties of Simulated Natural Fault Gouges under In Situ P-T Conditions
<p>Files named by in a format of "Uxxx_xx_xxMPa_xxC" refer to the original mechanical data recorded during experiment.</p> <p>The compressed package includes the files to perform numerical modeling, modeling results and the experimental data for comparison. To replicate the numerical modeling, readers can open the COMSOL project file (".mph" file) using COMSOL software (version >5.4) then input the parameters for the boundary conditions, such as the temperature, load-point velocity, oscillation amplitude and frequency. </p>
Recognized Fault Locations and Attributes, Getaberget, Åland Islands
<p>We mapped faults using their secondary indicators such as damage zones and secondary fracturing at Getaberget shoreline, Åland Islands, Finland.</p> <p>Coordinates are in x and y -columns in<em> EPSG:3067 ETRS-TM35FIN</em> coordinate system.</p> <p>The field mapping and photos were taken as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the summers of 2020 and 2021.</p>
Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"
<p>Artifact for the paper "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"</p> <p>The package contains:</p> <ul> <li>example files for all static and dynamic fault tree models</li> <li>installation instructions for the three tools</li> <li>scripts to perform the benchmarking</li> <li>detailed result tables</li> </ul>
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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)
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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.