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562 results for “faulting”
Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications
<p><strong>Data description of contamination measurements with lidar sensors RIEGL LD05-A20 and Ouster OS1-64</strong></p> <p><em><strong>Photos of the measurement setup</strong></em></p> <p>We provide photos of the measurement setup and the contaminations applied in the folder "/photos".</p> <p> </p> <p><em><strong>Riegl LD05-A20 data</strong></em></p> <p>The data of the Riegl LD05-A20 can be found in the folder "riegl_LD05-A20_full_waveforms" - one file per experiment. An example notebook for reading the data is provided in "/notebooks/example_riegl_LD05-A20.ipynb". Note that the files contain only the prominent peaks of the full waveform calculated by the V08Wave software provided by RIEGL. If you are interested in the entire full waveforms, please contact the authors.</p> <p> </p> <p><em><strong>Ouster OS1-64</strong></em></p> <p>The data of the Ouster OS1-64 can be found in the folder "ouster_OS1-64_point_clouds" - one folder per experiment. An example notebook for reading the data is provided in "/notebooks/example_ouster_OS1-64.ipynb". The python package <strong><em>pointcloudset</em></strong> (https://github.com/virtual-vehicle/pointcloudset) and its documentation is suggested for further data analytics of the point cloud data.</p> <p> </p>
Regional seismicity (ML≥1.0) from 2008 to 2022 for the Haiyuan fault system
<p>This data is the regional seismicity (ML≥1.0) from 2008 to 2022 from the article "Strain Accumulation and Release on the Haiyuan Fault System from Joint Analysis of InSAR, GPS and Seismological Observations".</p>
Surface velocities due to the Southern San Andreas Fault from Sentinel-1 InSAR data
<p>Line of sight (LOS), fault-parallel, and vertical velocities in the area around the Southern San Andreas Fault in California, USA. Gzipped tar archive. All data are in Generic Mapping Tools (GMT) Netcdf format. </p>
Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems
<p>These are the datasets for our ESEC/FSE'22 paper "Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems." In each dataset, <code>graph.yml </code>or <code>graphs/*.yml</code> are FDGs, <code>metrics.csv</code> is metrics, and <code>faults.csv</code> is failures (including ground truths).<code>FDG.pkl</code> is a pickle of the FDG object, which contains all the above data. Note that the pickle files are not compatible in different Python and Pandas versions. So if you cannot load the pickles, just ignore and delete them. They are only used to speed up data load.</p> <p>See more at https://github.com/NetManAIOps/DejaVu</p>
Combined U-series and in situ U-Pb dating of fault-related carbonates for reconstructing a long-term history of fault activity in response to SE Tibetan Plateau brittle deformation
<p>Table S1. Analytical conditions for LA-ICP-MS U-Pb dating</p> <p>Table S2. Analytical conditions for LA-ICP-MS elemental mapping</p> <p>Table S3.<em> In situ </em>calcite LA-ICAPMS U-Pb dating data</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>
Fault strength and rupture process controlled by fault surface topography
<p>Experimental source data for the study "Fault strength and rupture process controlled by fault surface topography"</p>
Evidence of Jurassic extension in NW Argentina: Characterization of fault-related strata at the Salta Group base using sandstone provenance and zircon U–Pb geochronology
<p>Supporting information accompanying the publication "Evidence of Jurassic extension in NW Argentina: Characterization of fault-related strata at the Salta Group base using sandstone provenance and zircon U–Pb geochronology" published in Journal of South American Earth Sciences. The dataset contains information on analytical procedures of detrital zircon U-Pb analyses.</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>
UAV-based DEM across the Peiku Co-Gyirong Rift normal faults, southernmost Tibetan
<p>These data are the unmanned aerial vehicle (UAV) topography surveying data (.tif) of several offset fluvial terraces/fans and lake shorelines across the Peiku Co-Gyirong Rift normal faults. The UAV data were acquired by ~100-200-m-high aerial photographs using a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK. High-resolution digital elevation models (DEM) were produced by Agisoft Metashape Professional software.</p> <p> </p>
Robustness assessment via simulation-based fault injection of the implementation level models of the LEON3, MC8051, and PIC microcontrollers in presence of stuck-at, bit-flip, pulse, and delay fault models
<p>This dataset package contains the results of fault injection experiments for RTL and implementation-level models 3 microprocessors (LEON3, MC8051, PIC).</p> <p><strong> 1. Package contents:</strong><br> - raw traces of fault injection experiments: *.lst files<br> - analysis results grouped by fault models: *.html files in the /REPORT subfolders<br> - summary for each targeted HDL model and fault model: index.html in each design folder</p> <p>To facilitate the navigation through the contents, it is organized as a tree of *.html pages. The root page is 'index.html' in the archive root.<br> Additionally, to observe the raw traces for each experiment in the convenient form, the *.lst files are processed on the fly by custom python-script, returning the interactive *.html page. Each observation trace is a table, where: <br> - each row represents an observation vector, comprising {simulation time stamp}, {flags}, {internal state}, {outputs}.<br> - each cell is highlighted:<br> a) green if matches with reference trace (fault-free simulation),<br> b) red if mismatches with reference trace, denoting error for internals / failure for outputs,<br> c) violet in case of vector whose timestamp was not in reference (unexpected transition).<br> These highlighting options can be customized by modifying the linked *.css files.</p> <p> <br> <strong> 2. Installation</strong><br> 2.1 Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p>2.2 Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise install the relevant 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.<br> <br> 2.2 Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the resulting line:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>2.3 Unpack the *.zip package into the folder on the Web Server. For instance 'Web-server root folder'/ExperimentalResults.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>2.4 In the web-browser navigate to the root directory of extracted package:<br> http://localhost/ExperimentalResults/index.html</p> <p> </p> <p><strong>3. How to read the contents</strong><br> The root page contains links to different analysis reports, for each HDL design under study and considered fault models. <br> The pages on the first tree level, represent the summary for each injection campaign, describing the rate of failure modes, number of experiments, latencies, and supplementary info. <br> The pages on the second tree level are the detailed analysis reports for each experiment, detailing the fault target, parameters of injected fault, detected failure mode, number of errors, etc. <br> The cells of the first column are highlighted a) in green if injection did not cause the failure, b) in red otherwise. The links in this first column navigate to the detailed traces for each experiment. <br> The latter requires that Web-server is configured to execute the python-scripts (see section 2 - Installation); otherwise the raw traces (*.lst files in ./results folders) can be observed by any text editor (notepad++, etc.).<br> </p>
Probabilistic Fault Displacement Hazard Assessment materials
<p>The models, data, and information provided here were created as part of the Fault Displacement Hazard Initiative. We provide the Electronic Supplement for Chiou et al., 2023, CDF Fortran subroutines; the ArcGIS least-cost path (LCP) model and implementation guide, LCP MATLAB and Python scripts, and the LCP for 75 events in a shapefile and KMZ format for Thomas et al., 2023; and the Fortran code for Chiou et al. in review for Earthquake Spectra. </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>
Replication package of "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation"
<p>Replication package for the paper "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation" accepted at the Symposium on Search-based Software Engineering (SSBSE) 2024.</p> <p> </p> <p> </p>
High-resolution topography data and fault trace along the Elashan fault
<p> High-resolution digital elevation models (DEM) topography data extracted from the uncrewed aerial vehicle (UAV) of a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK, and GF-7 satellite stereo imagery. The trace of the Elashan fault is interpreted based on these high-resolution topography data.</p>
Pn velocity and anisotropic tomography models, and Moho depth of the Tanlu Fault Zone
<p>Our dataset includes Pn velocity and anisotropic tomography models, and Moho depth of the Tanlu Fault Zone.</p>
Arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas
<p>This file is the arrival-time data automatically picked up by PickNet of Pn wave in Tanlu Fault Zone and surrounding areas. Only these Pn arrival times are used for tomographic inversion.</p> <p> </p>
Data for "effects of fault contact heterogeneity on laboratory earthquake initiation and dynamic rupture"
<p>The second column in the files named by "Time_and_dLP", "Time_and_Mu0", "Time_and_Sigma0", and "Time_and_Tau0" indicate the along-fault loading point displacement (dLP), macroscopic friction coefficient (Mu0), macroscopic normal stress (Sigma0), and macroscopic shear stress (Tau0), respectively, measured in the loading apparatus. The first column in these files are time.</p> <p>Local fault displacement data are named by the form of, for example, "Event101_FaultDisplacement_L1(x=270mm)", which means that the fault displacement measured by Sensor L1 located at x=270 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_FaultDisplacement_Time".</p> <p>Local shear stress data are named by the form of, for example, "Event101_ShearStress_S1(x=-323.95mm)", which means that the shear stress measured by Sensor S1 located at x=-323.95 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_ShearStress_Time".</p>
Earthquake catalog at the Blanco Transform Fault Zone between 2012 and 2013
<p>The csv file provides an earthquake catalog derived from data of ocean-bottom seismometers operated between 2012 and 2013 at the Blanco Transform Fault Zone. The data file is in ASCII text format. The first row is a column header that describes the content of the catalog: Earthquake origin date, time, latitude, longitude, depth and local magnitude. Not all earthquakes have a local magnitude estimate due to data selection.</p>
Evolution of Hydromechanical Coupling from Quartz to Shale rich Faults and Implications for Slip stability
<p><span>Fault rock heterogeneity has a dominant effect on frictional and fluid flow properties of faults, yet the study of how fault hydromechanical properties are coupled, evolve with fault fabric and influence slip </span><span>behaviour</span><span> is still in its infancy. Here, we show that the increase in shale content within a quartz-rich experimental fault promotes a fabric ev</span><span>olution from a load-bearing granular framework to an interconnected foliated and clay-rich network. This fabric evolution causes a significant reduction in frictional strength, a marked decrease in permeability, and enhanced frictional stability. Fault sta</span><span>b</span><span>ility is enhanced by fault dilation associated to a reduction in pore fluid pressure within the low permeability, clay-rich faults. Our results indicate that fault rock fabric and associated hydromechanical properties exert a primary control on fault slip </span><span>behaviour</span><span>. The enhanced stability within pressurized clay-rich faults </span><span>matches</span><span> well with observations of aseismic slip during hydraulic stimulations of shales and models proposing dilation strengthening as a mechanism for the occurrence of slow slip events in shale rich sediments at shallow crustal levels. </span></p> <p> </p> <p><span>this repository contains the raw data for all experiments reported in Table 1. </span></p> <p><span>Furthermore, you are able to reproduce each figure by executing the Python code in each .zip file. </span></p> <p><span>For most of the codes you will have to install rawPy that is a specific Python package to manipulate our lab data and can be found at </span></p> <p><span>https://github.com/marcoscuderi/rawPy</span></p> <p><span>for any further request please do not hesitate to contact me at marco.scuderi@uniroma1.it</span></p>
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