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562 results for “Faults”
Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?"
<p># Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?"</p> <p>## Overview</p> <p>In mutation testing, faulty versions of a program are generated through automated modifications of source code. These mutants are used to assess and improve test suite quality, under the assumption that detection of mutants is indicative of a test suite's ability to detect real faults - i.e., that mutants and faults have a semantic relationship. Improving the effectiveness - in both cost and quality - of mutation testing may lie in better understanding this relationship, in particular with regard to how individual mutation operators (types) couple to real faults. </p> <p>In this study, we examine coupling between 32,002 mutants produced by 31 mutation operators and 144 real faults, using a scale based on number of failing tests and reasons for failure. Ultimately, we observed that 9.92% of the mutants are strongly coupled to real faults, and 51.03% of the faults have at least one strongly coupled mutant. We identify and examine mutation operators with the highest median coupling, as well as the operators that tend to produce non-compiling mutants, undetected mutants, and mutants that cause the most tests to fail outside of the tests that detect the actual fault. We also examine how coupling could be used to filter the set of operators employed, leading to potentially significant cost savings during mutation testing. Our findings could lead to improvements in how mutation testing is applied, improved implementation of specific mutation operators, and inspiration for new mutation operators. </p> <p>## Data Contained in This Package</p> <p>- mutant_data.csv</p> <p>This dataset contains the coupling results for all mutants considered in our experiments. It contains the following attributes for each mutant:</p> <p>-- Project name from Defects4J<br> -- Fault number from Defects4J<br> -- Mutation ID<br> -- Mutation operator<br> -- Number of trigger tests for the fault (tests that detect the real fault)<br> -- Number of failing test cases for the mutant (-1 indicates a compilation error)<br> -- The number of failing trigger tests for the mutant<br> -- The number of trigger tests that fail for the same reason the tests failed for the real fault.<br> -- The number of failing non-trigger tests.<br> -- The categorization of coupling. In order: Compile Error, Not Detected, No Substitution, Partial Test Substitution + Additional Tests Fail, Partial Test Substitution, Partial Substitution + Additional Tests Fail, Partial Substitution, Test Substitution + Additional Tests Fail, Test Substitution, Strong Substitution + Additional Tests Fail, Strong Substitution. </p> <p>- mutant_logs/{Project}/{Project}{Fault Number}output.txt</p> <p>The raw output log that resulted from executing test cases for each mutant for each case example used from Defects4J. Used to generate the dataset discussed above. Scripting for generating the dataset is also included.</p>
2002 and 2022 fault ruptures along the Timpe faults system (Mt. Etna)
<p>This dataset includes two shape files with ground ruptures observed on Mt. Etna in 2002 and 2022, in particular:</p> <ul> <li>the surface faulting, along the Santa Venerina, San Giovanni Bosco, Guzzi, and Scillichenti Faults accompanying the October 29 2002 earthquakes;</li> <li>the surface faulting along the creeping Scalo Pennisi (SCA) Fault observed on 29 October 2002 and 8 February 2022;</li> </ul> <p>Each shape file is associated with a database that contains information on: strike, length (m), heave (i.e., horizontal displacement in cm), throw (i.e., vertical displacement in cm), net slip displacement (cm), slip trend and plunge from certain piercing points.</p> <p>The dataset is associated with the paper: <em>"Aseismic creep and gravitational sliding on the lower eastern flank of Mt. Etna: insights from the 2002 and 2022 fault rupture events between Santa Venerina and Santa Tecla" </em>by G. Tringali, Bella, D., Livio F., Ferrario M. F., Groppelli G., Pettinato R., Michetti A. M.</p> <p> </p>
Toward understanding tectonic and geometric control on the Lenglongling fault from inter- and co-seismic InSAR observations
<p>The datasets include the interseismic (2014-2021) and coseismic InSAR observations to characterize the interseismic slip-rate along the Qilian-Haiyuan fault, the fault geometry and coseismic slip distribution for the 2022 Menyuan earthquake.</p>
Footwall Relief Measurements for faults in the Zomba Graben, Malawi
<p>Footwall releif measurements for faults in the Zomba Graben Malawi. Footwall relief was measured every 1 km along strike using stacked profiles of TanDEM-X topographic data that had been sampled every 100 m along strike. We measured the difference in elevation between the highest point on the footwall within 3 km of the fault surface trace, and the elevation of the fault itself.</p> <p> </p> <p>#1 - Longitude</p> <p>#2 - Latitude</p> <p>#3 - Footwall Relief (m)</p> <p>#4 - Uncertainty (m)</p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</p>
The effect of shear displacement and wear on fault stability: laboratory constraints
<p>This is the ReadMe file corresponding to the study entitled:<br> "The effect of shear displacement and wear on fault stability: laboratory constraints"<br> By Noël C., Giorgetti c., Scuderi M.M., Collettini C. and Marone C.</p> <p>This study has been submited in JGR: Solid Earth </p> <p>This Read-Me file has been last edited in December 2022</p> <p>This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@uniroma1.it</p>
X-ray computed microtomographic (XRCT) images of a fault core that slipped during the 1726 San Andreas faultzone earthquake
<p>Uploaded are x-ray computed microtomographic (XRCT) images used to examine solid-fluid interactions within one of the near-surface fault cores that slipped during a circa (ca.) 1726 San Andreas Fault zone earthquake. The study site is 16 km northwest of Bombay Beach, California (33.45873, -115.8560), and our sample, collected at a depth of 1.2 m below sea level, is from a trench that exposes deposits of ancient Lake Cahuilla. The ca. 1726 earthquake occurred during a highstand of ancient Lake Cahuilla; our study site was ~55 m below the lake's surface at the time. Crustal deformation caused by the ca. 1726 earthquake has been documented for at least 85 km along the southernmost San Andreas fault zone, which has been used, alongside other observations, to constrain the earthquake's size to a magnitude 7.2 or larger with offsets on the order of ~3 m. Since the ca. 1726 earthquake, creep and triggered slip have occurred along the section of the fault we study, with estimates of ~3 mm/yr of motion over the last ~160 years.</p> <p>We acquire XRCT images at the Advanced Light Source, Lawrence Berkeley National Lab, on beamline 8.3.2. Imaging uses a 50 mm LuAG scintillator, PCO Edge camera, and 1X Nikon lens. We image with white light x-rays, 13 ms exposure times, and 2625 projections through 180-degree continuous sample rotations. This produces 1280 two-dimensional image slices with voxels' linear dimensions of 3.24 microns. We reconstruct images and perform ring removal, center of rotation optimizations, and outlier removal using TomoPy. We name the sample FT_50_4_ZZZZ, where ZZZZ represents the image slice number; increasing numbers represent increasing distance into the outcrop.</p>
"Analysis of near-field stresses in an analogue strike-slip fault model" Dataset
<p>This dataset contains both raw data and preliminary processed data. Overall, the dataset is divided into three parts: loading device data, internal strain brick data and VIC-2D data.</p>
Data Set for Sandanbata et al. (2023: GRL) entitled "Two volcanic tsunami events caused by trapdoor faulting at a submerged caldera near Curtis and Cheeseman Islands in the Kermadec Arc"
<p><strong>Descriptions</strong></p> <p>This dataset contains supplementary materials for the manuscript under revision for Geophysical Research Letters; the preprint has been uploaded to ESS Open Archive:</p> <ul> <li>Sandanbata, O., Watada, S., Satake, K., Kanamori, H., & Rivera, L. (2023). Two volcanic tsunami events caused by trapdoor faulting at a submerged caldera near Curtis and Cheeseman Islands in the Kermadec Arc. <em>Geophysical Research Letters</em>, 50, e2022GL101086. <a href="https://doi.org/10.1029/2022GL101086">https://doi.org/10.1029/2022GL101086</a></li> </ul> <p>We constructed a source model for the 2017 earthquake at Curtis caldera in the Kermadec Arc. The dislocation distributions and source geometries of this source model, presented in Figure 3, are contained in this dataset.</p>
Physical state of water controls friction of gabbro-built faults
<p>Experimental data</p> <p>six columns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, axial displacement (mm), pore pressure (MPa) and temperature (℃). </p>
RSW gun fault prediction benchmark data set (demo)
<p>The resistance spot welding (RSW) welding gun fault prediction benchmark data set has 72 multivariate time series in the training set and 8 in the testing set. Each time series length 604800 sampled at 1 Hz with missing values and has 20 dimensions (c1-c19 and the error code). We retain the missing value and the outliers of the welding gun time series for the potential of imputation research in the future.<br> This data set supports an academic paper named 'benchmark study for welding gun fault prediction'.</p> <p><strong>Feature name and explanation:</strong></p> <p>c1 : Electrode cap offset;</p> <p>c2 : Electrode force;</p> <p>c3 : Electrode position;</p> <p>c4 : Force build-up;</p> <p>c5 : Balance pressure;</p> <p>c6 : Friction;</p> <p>c7 : Maximum aperture;</p> <p>c8 : Maximum electrode force;</p> <p>c9 : Mtart friction;</p> <p>c10 : US2;</p> <p>c11 : Welding point count;</p> <p>c12 : Position count;</p> <p>c13: Setpoints of counterbalance pressure;</p> <p>c14: Setpoints of electrode force;</p> <p>c15 : Setpoints of electrode position;</p> <p>c16: Setpoints of sheet thickness;</p> <p>c17 : Setpoints of velocity;<br> c18: Setpoints of force build-up;<br> c19 : Offset value in robot.</p> <p><strong>Machine Learning Task:</strong><br> This dataset is suitable for a time series forecasting task, where machine learning models can be trained to predict future welding parameters based on the provided welding parameters time series in history. </p> <p><strong>Code for quick start:</strong></p> <p><a href="https://zenodo.org/record/7655025">https://zenodo.org/record/7655025</a></p> <p>If you want to have an overview of the data before downloading all of it, you can download only the files with the word "Damo" in the file name.</p> <p>For any question, please contact 1910633@stu.neu.edu.cn</p>
L-TOWN simulated measurement without faults or cyber-attacks for scenarios with masking
<p>Additional resources for repository <a href="https://github.com/asztyber/wdn-simulation">asztyber/wdn-simulation</a></p> <p>Required to run scenarios with masking.</p>
Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes - Datasets
<p>Datasets associated with manuscript "Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes" by Morton et al. (2023), submitted to the <em>Journal of Geophysical Research: Solid Earth</em>. Three datasets are present in this upload:</p> <p><strong>ds01.xlsx</strong>: Catalog of 5,282 detected earthquakes along the Cascadia subduction margin, ordered by time. Events were located using Hypoinverse (Klein, 2002). Columns of the catalog are: Cascadia Initiative deployment year, Origin Time String (YYYYMMDDhhmmss), Origin Time (Year, Month, Day, Hour, Minute, Second), Latitude, Longitude, Event Depth (km), Duration Magnitude (Md), Number of P and S arrival picks with weights > 0.1, Maximum Azimuthal Gap (deg.), Distance to the Nearest Station (km), Travel Time Residual RMS (s), Horizontal Location Error (ERH; km), Vertical Location Error (ERZ; km), Focal Mechanism if applicable (Strike, Dip, Rake; deg.), Plate Designation (Slab, Interface, or Upper Plate), and Previous Existence in Regional Catalogs. Duration magnitudes that could not be constrained are listed as -9. Detected earthquakes that had previously been reported in other catalogs are listed as "Catalog" or "Stone" form the regional or Stone et al. (2018) catalogs, respectively; Those used as template events are marked as "T" or "ST", for those from regional catalogs or the Stone et al. (2018) catalog, respectively, in the last column.</p> <p><strong>ds02.xlsx</strong>: Table of earthquakes chosen as template events for subspace scanning from regional (NEIC, ANF, PNSN, CNDC) and Stone et al. (2018) catalogs. Columns of the template event table are: Catalog Source (T for regional, ST for Stone et al. 2018), Cascadia Initiative (CI) Deployment Year, Template Cluster ID, Date, Time (UTC), Catalog Location (Latitude, Longitude, Depth), Catalog Magnitude, and Whether the Template Event was Detected. Some of the templates were detected but were not included in the final catalog because the travel time residual RMS was greater than 1s and are noted in the table as "poorly located".</p> <p><strong>ds03.xlsx</strong>: Table of seismic stations used in subspace detection scanning, identified by the CI deployment year, Template Cluster ID, Station SEED, and Network Codes. Stations are listed with the corresponding high-pass (HP) or band-pass (BP) filter applied before scanning to maximize the signal-to-noise ratio.</p> <p>References</p> <p>Klein, F. W. (2002). <em>User’s Guide to HYPOINVERSE-2000, a Fortran Program to Solve for Earthquake Locations and Magnitudes</em> (Open File Report 02-171). U.S Geological Survey. https://doi.org/10.3133/ofr02171</p> <p>Stone, I., Vidale, J. E., Han, S., & Roland, E. (2018). Catalog of off-shore seismicity in Cascadia: Insights into the regional distribution of microseismicity and its relation to subduction processes. <em>Journal of Geophysical Research: Solid Earth, 123</em>, 1–12. https://doi.org/10.1002/2017JB014966</p>
The HELPOS Fault Database: a new contribution to seismic hazard assessment in Greece
<p>In seismically-active regions such as Greece, the mapping of active faults is a key step to assess seismic hazards and evaluate deterministic ground motion scenarios for infrastructure works, pipeline designs and other constructions of critical importance. Here, we present a comprehensive database of active onshore and offshore faults in Greece based on existing studies and GIS geospatial mapping using geological, geophysical, seismological and geomorphological criteria. The design and population of the database follows the NOAFaults concept <a href="http://doi.org/10.5281/zenodo.3483136">http://doi.org/10.5281/zenodo.3483136</a> and development in ARCGIS environment. The HELPOS database includes over 550 faults with simplified (linear) traces and lengths between 8 – 108 km (onshore part) together with their corresponding 2D rupture planes. Additional information includes parametric data such as maximum expected magnitude, slip rate, length, width, strike, dip angle, last seismic event, rupture depth (to top-fault) and fault kinematics. A particular aim of the HELPOS Fault database has been an update of the seismic sources model for the seismic hazard assessment of Greece considering shallow earthquakes, which involves modeling surface fault traces in terms of seismic sources at depth. The fault database is a major contribution to HELPOS with applications among others in volcano-tectonic settings, urban planning, paleoseismology, landscape processes, and in the study of active tectonics, deformation and interactions between overriding plate (Aegean) faults and the Hellenic subduction.</p> <p><strong>In this version of the database (v1.8) we include the onshore fault traces and rupture planes and the offshore fault traces</strong>.</p> <p>We acknowledge funding by project "HELPOS - Hellenic Plate Observing System” (MIS 5002697) which was funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).</p>
Datasets associated with a study of extensional faulting around Kolumbo Volcano, Aegean Sea
<p>This contribution of data includes 56 different data files of various formats, plus one excel file ("Data_Inventory_and_Descriptions_Kolumbo_Faulting.xlsx") which provides all of the necessary metadata for each of the 56 data files. The excel file should be used as a readme file, detailing key information for each file including format, georeferencing information, and relevant software for loading and viewing the data. The excel file also shows how each file is related to an associated figure in a manuscript currently being submitted for peer review. Once that paper is published, this description will be updated with the doi of the paper.</p>
Rock magnetic data sets for Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism
<p>The rock magnetic data sets in "Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism" was studied, including the density, magnetic susceptibility, NRM, Q ratio and other parameters of rock samples , as well as the thermomagnetic curves, hysteresis loops, FORCs, AF and TD demagnetization.</p>
Supplementary materials for: Multiple ruptures on the North Sofia fault at Gorni Bogrov from scarp profile and shallow geophysics
<p>The data set contains resistivity data of two profiles that were measured at Gorno Bogrov site in the Sofia basin in Bulgaria. The resistivity survey was performed to study the North Sofia fault.</p> <p>The resistivity data in files r1ohm.tx and r2ohm.txt should be inverted using the BERT software (http://resistivity.net/) with configuration files r1cfg.txt and r2icfg.txt, respectively. The file bog2.txt contains fault coordinates for constraining the inversion mesh of the shorter profile (files r2ohm.txt, r2icfg.txt).</p> <p>The data set was collected within the project Assessment of Earthquake Ground Motion Amplification in the Sofia Basin (<a href="http://sofiabasin.atwebpages.com/">http://sofiabasin.atwebpages.com/</a>), funded by the Bulgarian National Science Fund, contract number KP-06-N64/1 from 15.12.2022.</p>
Contribution of the Altyn Tagh Fault to tectonic deformation within the Qilian Shan, northern Tibetan Plateau: Implications from the electrical anisotropic structure
<p>The dataset contains five folders. They are ‘MT’, ‘aniinv’, ‘isoinv’, ‘sensitivity_test’, ‘syn_mod_test’, respectively. The ‘MT’ folder includes the observed impedances for each MT site. In the ‘aniinv’ folder, there are three sub-folders include ‘azimu_ani_inv’, ‘gener_ani_inv’, ‘verti_ani_inv’, indicating the inversion results for azimuthal, general, and vertical anisotropic inversions, respectively. The ‘isoinv’ folder contains results for isotropic inversion. The ‘sensitivity_test’ folder contains modified models and their responses for sensitivity tests of anomalies. The ‘syn_mod_test’ folder contains a synthetic model constructed according the final model, the responses of this model, and the recovering for this model. In each folder, there is a ‘readme.txt’ file describing the details of individual files.</p> <p>The 'Software Availability.txt' file illustrates that the software used in 3D anisotropic inversion is available to academic community.</p>
Illuminating the Hierarchical Segmentation of Faults through an Unsupervised Learning Approach applied to clouds of earthquake hypocenters [Dataset]
<p>Data repository to the preprint “Illuminating the Hierarchical Segmentation of Faults through an Unsupersived Larning Approach applied to clouds of earthquake hypocenters” by Piegari et al. (2023), including the datasets for the three analyzed earthquake catalogs.</p>
Strain localization in sandstone-derived fault gouges under conditions relevant to earthquake nucleation
<p>Constraining strain localization and the growth of shear fabrics within brittle fault zones at sub-seismic slip rates are important for understanding fault strength and frictional stability. We conducted direct shear experiments on simulated sandstone-derived fault gouges at an effective normal stress of 40 MPa, pore fluid pressure of 15 MPa, and temperature of 100°C. Using a passive strain marker and X-ray Computed Tomography (XCT), we analyzed the spatial deformation of the gouge samples obtained from the strain-hardening stage to strain-softening stage to steady-state at shearing velocities of 1, 30, and 1000 µm/s. We developed a machine-learning-based automatic boundary detection method to recognize the shear zone fabrics and quantify the slip partitioning between each fabric element. Our results show that R1 and Y (or boundary) shears are the two major shear zone fabrics. At velocities of 1 and 30 µm/s, the relative amount of slip on R1 shears is displacement dependent and increases to ~20% at the strain-softening stage and then decreases to ~10–18% at steady-state. This trend is absent at high velocity with an amount of ~18% through all investigated stages. At all velocities, the relative amount of slip on Y and boundary shears increases linearly with displacement to a total of more than 50% at steady-state. Our study provides constraints for the development of the active slip zone, which is an important input parameter for the heat budget for small-magnitude earthquakes with limited slip (mm-dm), such as those occurring in induced seismicity.</p>
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