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562 results for “faults”
dataset used in article entitiled "Heterogeneous shear strength around mainshock fault of the 2000 Western Tottori earthquake (M 7.3) through dense seismic observations"
<p>See README.doc for description about the files.</p>
10Be data for the Hyde and Dunstan faults
<p>Supporting data for Griffin, Stirling, Wilcken and Barrell: Late Quaternary slip rates for the Hyde and Dunstan faults, southern New Zealand: implications for strain migration in a slowly deforming continental plate margin</p>
Cyclic fault slip under the magnifier: Co- and postseismic response of the Pamir front to the 2015 Mw7.2 Sarez, Central Pamir, earthquake
<p>The file Alai_GPS_timeseries.zip contents two text files with time series data for GPS observation of the Alai network for the periods from July 2016 to June 2018 and from July 2018 to June 2020. The data were used to determine the velocities of post-seismic displacements of the Earth's surface in the periods from July 2016 to June 2018 and from July 2018 to June 2020.<br> Each of the other 10 zip files ending in baselines.zip includes time series of daily values of the baselines between one Alay GPS station and Asian stations not affected by the 2015 Sarez earthquake M7.2w, for a period spanning at least 60 days before and after it . The data was used to calculate the coseismic displacements caused by this earthquake.</p>
SEM-BSE and Photomicrographs of Slickenlines from Plan de Platanos, Big Piute and Waterman Hill Faults
<p>Photomicrographs and energy dispersive X-ray spectroscopy of Plan de Platanos (<strong>PP</strong>), Big Piute (<strong>BP</strong>) and Waterman Hill (<strong>WH</strong>) Fault Slickenlines</p> <p><strong>PP</strong>---> composition---->andesite</p> <p><strong>BP</strong>---> composition ----->quartzite</p> <p><strong>WH</strong>--->composition ----->mylonitized metasediments</p> <p><strong>Abbreviations:</strong></p> <ul> <li><strong>ppl-</strong>----->plane polarized light</li> <li><strong>xpl-</strong>----->plane polarized light</li> </ul> <p><strong>Orientation: </strong>We use Tikoff et al (2019) naming convention for thin section orientation.</p> <ul> <li><strong>XZ-</strong>-parallel to lineations</li> <li><strong>YZ-</strong>-perpendicular to lineations</li> </ul> <p><strong>Instruments:</strong></p> <ul> <li>ZEISS AX10 Petrographic microscope</li> <li>ZEISS Merlin HR-SEM with backscattered electron (BSE) sensor and energy dispersive X-ray spectroscopy (EDS)</li> </ul> <ul> </ul> <p> </p> <p>Tikoff, B., Chatzaras, V., Newman, J., & Roberts, N. M. (2019). Big data in microstructure analysis: Building a universal orientation system for thin sections. Journal of Structural Geology, 125, 226–234. https://doi.org/10.1016/j.jsg.2018.09.0</p>
The 2021 and 2022 Fukushima-Oki Earthquake Doublet: Reactivations of the Bending-Related Faults Inside the Japan Trench Subducting Slab
<p>The strong-motion (K-NET, KiK-net and S-net) waveforms and teleseismic <em>P</em> waves that were processed and used in the joint inversions of the 2021 <em>M</em><sub>w</sub>7.1 and 2022 <em>M</em><sub>w</sub> 7.3 Fukushima-Oki earthquakes are included in this repository.</p>
Hu et al., 'The productivity of induced seismicity is controlled by injection fluid volume and preexisting faults in southern Sichuan Basin'
<p>1. Earthquake catalog:Earthquake catalogs within 2 kilometers of each platform (N5 and N7). The 10 columns from left to right are year, month, day, hour, minute, second, latitude, longitude, depth and magnitude.</p> <p>2. Injection Volume of all wells:Fracturing injection volume per stage per horizontal well. The 4 columns from left to right are Serial number of well & pads, Fracturing section number, Fracturing time span and Total injection volume (m3).</p> <p>3. The range of Spatiotemporal Association Filter (SAF):SAF vertex coordinates for six individual fracture well groups. The first column is the boundary of horizontal well trajectory and the boundary of Spatiotemporal Association Filter (SAF) from top to bottom. The second column is the names of the four vertices. Columns 3 through 12 show longitude and latitude for each well group, respectively.</p> <p>4. Waveform Data:Waveforms of the selected earthquakes with focal mechanism solutions.</p>
Data Set for Sandanbata et al. (2022: JGR-Solid Earth) entitled "Sub-decadal Volcanic Tsunamis Due to Submarine Trapdoor Faulting at Sumisu Caldera in the Izu-Bonin Arc"
<p><strong>Descriptions</strong></p> <p>This dataset contains supplementary materials for the following research article:</p> <ul> <li>Sandanbata, O., Watada, S., Satake, K., Kanamori, H., Rivera, L., & Zhan, Z. (2022). Sub-decadal volcanic tsunamis due to submarine trapdoor faulting at Sumisu caldera in the Izu–Bonin Arc. <em>Journal of Geophysical Research: Solid Earth</em>, 127, e2022JB024213. <a href="https://doi.org/10.1029/2022JB024213">https://doi.org/10.1029/2022JB024213</a></li> </ul> <p>We construct earthquake source models for the 2015 earthquake at Sumisu caldera in the Izu-Bonin Arc. Four source models, presented in Figures 4a, S8a, S9a, and S14a, are contained in this dataset. </p>
Dog Valley Fault Traces
<p>This is a shapefile containing linework of the Dog Valley Fault in Northern California. </p>
Numerical data for dynamic rupture simulations of coseismic slickenlines on non-planar and rough faults
<p>Numerical data used to make Figures in the manuscript entitled "Dynamic simulations of coseismic slickenlines on non-planar and rough faults (Aoki et al., GJI)"</p>
Dynamic rupture simulation of Wenchuan-Maoxian and Lixian Fault
<p>The Video shows the slip rates of 4 earthquake cases on the LMS fault system. The ruptures started from different hypocenters and results in different earthquake magnitudes. </p>
Dataset for "Emerging tremors and increasing seismic noise precede micro-earthquakes triggered in a fluid-activated shale fault slip experiment (2015, Mt Terri URL, Switzerland)"
<p>This dataset contains the raw data of the injection experiment, performed in the Mt Terri Underground Platform in 2015 and used in the article:</p> <p><strong>De Barros, L., </strong>Guglielmi, Y., F. Cappa, C. Nussbaum, J. Birkholzer, 2023. Induced microseismicity and tremor signatures illuminate different slip behaviors in a natural shale fault reactivated by a fluid pressure stimulation (Mont Terri), <em>Geophysical Journal International</em>, 10.1093/gji/ggad231<br> <br> From a horizontal gallery, three vertical boreholes allowed the deployment of an injection probe (called SIMFIP; Guglielmi et al., 2014) and the monitoring sensors in the upper compartment of a N50°-60°SE fault zone. The 2.4 m long injection chamber of the SIMFIP probe was centered at 340.6 m depth, where a 3D displacement sensor was anchored on the borehole walls. A second SIMFIP probe is located 3.1 m northwest of the injection at a depth of 337.65 m, with another deformation sensor. Both deformation sensors measured the full strain tensor thanks to a Bragg optic fiber network, jointly with a fluid pressure sensor. A third borehole, located 2 m north of the monitoring probe, was dedicated to seismic monitoring. Two sets of collocated sensors, composed of a vertical geophone, a 3C accelerometer and an acoustic sensor, were positioned 9 m apart, above and below the main fault zone. These seismic sensors have a flat response in the ranges 0.01-0.5 kHz, 0.01-4 kHz and 0.5-10 kHz, respectively. Finally, the flowrate and pressure were also measured at the injection pump, located in the gallery.<br> For more details on the injection, we refer the reader to:<br> • Jeanne, P., Guglielmi, Y., Rutqvist, J., Nussbaum, C., Birkholzer, J., 2018. Permeability Variations Associated With Fault Reactivation in a Claystone Formation Investigated by Field Experiments and Numerical Simulations. J. Geophys. Res. Solid Earth 123, 1694–1710. https://doi.org/10.1002/2017JB015149<br> • Guglielmi, Y., Nussbaum, C., Cappa, F., De Barros, L., Rutqvist, J., Birkholzer, J., 2021. Field-scale fault reactivation experiments by fluid injection highlight aseismic leakage in caprock analogs: Implications for CO2 sequestration. Int. J. Greenh. Gas Control 111, 103471. https://doi.org/10.1016/j.ijggc.2021.103471<br> • Guglielmi, Y., Nussbaum, C., Jeanne, P., Rutqvist, J., Cappa, F., Birkholzer, J., 2020. Complexity of Fault Rupture and Fluid Leakage in Shale: Insights From a Controlled Fault Activation Experiment. J.Geophys. Res. Solid Earth 125, e2019JB017781. https://doi.org/10.1029/2019JB017781<br> • Guglielmi, Y., Cappa, F., Lançon, H., Janowczyk, J.B., Rutqvist, J., Tsang, C.F., Wang, J.S.Y., 2014. ISRM Suggested Method for Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP): Using a 3-Components Borehole Deformation Sensor. Rock Mech. Rock Eng. 47, 303–311. https://doi.org/10.1007/s00603-013-0517-1</p>
Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific
<p>Supplementary Information</p> <p>for</p> <p>Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific</p>
Datafiles for "Ring Fault Slip Reversal at Bárðarbunga Volcano, Iceland: Seismicity during Caldera Collapse and Re-Inflation 2014-2018"
<p><strong>Datafiles for the manuscript "Ring Fault Slip Reversal at Bárðarbunga Volcano, Iceland: Seismicity during Caldera Collapse and Re-Inflation 2014-2018" by Glastonbury-Southern, E., Winder, T., White, R.S., and Brandsdóttir, B., submitted for publication in Geophysical Research Letters.</strong></p> <p>This includes cut waveform data in miniseed format from stations operated by the University of Cambridge for the 30 earthquakes included in the study, as listed in Table S1.</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" accepted to the International Conference on Software Testing (ICST) 2023. The replication package is at https://github.com/FlowSs/RLMutation.</p>
Surface displacement measurements and fault slip models for the 1997 Mw 7.2 Zirkuh earthquake
<p><strong>Source models of the 1997 Mw 7.2 Zirkuh earthquake inferred from InSAR and optical correlation displacement field </strong></p> <p><strong>Introduction</strong></p> <p>We provide the InSAR and optical correlation displacement fields of the 1997 Mw 7.2 Zirkuh earthquake (NE Iran).<br> The InSAR data have been processed by Sudhaus and Jonsson (2011) and the optical correlation data by Marchandon et al. (2017). <br> We also provide the different fault slip models for the earthquake inferred from these data and published in Marchandon et al. (2017). <br> Finally, we include the source code of the genetic algorithm used in Marchandon et al. (2017) to infer the uniform slip models of the Zirkuh earthquake.<br> This genetic algorithm (Sudhaus and Jonsson, 2011) allows estimating the geometry and a uniform slip value for each segment of the fault. The Abiz fault, that broke during the Zirkuh earthquake, <br> is a complex structure with many fault strike variations that requires 16 segments to be properly modelled. Thus, a penalty function is implemented to constrain the algorithm to sample models<br> with limited dip angle fluctuations between neighboring segments (Sudhaus and Jonsson, 2011). The rupture is modelled as a dislocation embedded in an elastic half-space (Okada, 1992). </p> <p>For further information about the data, the method and the model results, see Marchandon et al. (2017). </p> <p><strong>Content</strong></p> <p>Data: InSAR data, optical Correlation data, weighting matrix files in .mat format, matlab script to plot the data. <br> Fault: Abiz fault segment locations file in .mat format and matlab script to plot the fault.<br> Scripts: All scripts needed to run the Non-linear optimization.<br> Models: Fault slip models for the Zirkuh earthquake and matlab script to plot them. </p> <p><strong>References</strong></p> <p>Sudhaus, H., and S. Jonsson (2011), Source model for the 1997 Zirkuh earthquake (Mw=7.2) in Iran derived from JERS and ERS InSAR observations, Geophysical Journal International, <br> 185(2), 676–692, doi:10.1111/j.1365-246X.2011.04973.x.</p> <p>Marchandon, M., Vergnolle M., Sudhaus, H., and Cavalié., O., (2017) Fault geometry and slip distribution at depth of the 1997 Mw 7.2 Zirkuh earthquake: <br> contribution of near-field displacement data, accepted with minor revisions at Journal of Geophysical Research: Solid Earth. </p> <p>Okada, Y. (1992), Internal deformation due to shear and tensite faults in a half-space, Bulletin of Seismological Society of America, pp. 1018–1040.</p> <p> </p>
Satellite Fault Mapping of 1968 & 1979 Dasht-e Bayaz earthquake ruptures, Iran
Open the record for dataset details and reuse information.
Supplemental Material for Context Switch Sensitive Fault Localization
Open the record for dataset details and reuse information.
Caprock genesis in hydrothermal systems via alteration-controlled fault weakening and impermeabilization
<p>Raw experimental data</p>
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
<p>Over the past few years, increasingly complex machine learning methods have been applied for various Software Engineering (SE) tasks, particularly for the important task of automated fault prediction and localization. It, however, becomes much more difficult for scholars to reproduce the results that are reported in the literature, especially when the applied deep learning models and the evaluation methodology are not properly documented and when code and data are not shared. Given some recent---and very worrying---findings regarding reproducibility and progress in other areas of applied machine learning, this study aims to analyze to what extent the field of software engineering, in particular in the area of software fault prediction, is plagued by similar problems. We have therefore conducted a systematic review of the current literature and examined the level of reproducibility of 56 research articles that were published between 2019 and 2022 in top-tier software engineering conferences. Our analysis revealed that scholars are apparently largely aware of the reproducibility problem, and about two-thirds of the papers provide code for their proposed deep-learning models. However, it turned out that in the vast majority of cases, crucial elements for reproducibility are missing, such as the code of the compared baselines, code for data pre-processing, or code for hyperparameter tuning. In these cases, it, therefore, remains challenging to reproduce the results in the current research literature exactly. Overall, our meta-analysis, therefore, calls for improved research practices to ensure the reproducibility of machine-learning-based research.</p>
Data and Code for "Fault Network Geometry Influences Earthquake Frictional Behavior"
<p>This dataset contains the data and code necessary to reproduce the results presented in the paper, "Fault-Network Geometry Influences Earthquake Frictional Behavior" <em>Nature</em> (2024), authored by J. Lee, V. C. Tsai, G. Hirth, A. Chatterjee, and D. T. Trugman.</p> <p>https://doi.org/10.1038/s41586-024-07518-6</p>
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Allen Brain Atlas
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OpenNeuro
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