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30 results for “active fault”
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>
Late Quaternary activity of the NW Cardrona Fault, Otago, New Zealand - Supplements S1 and S2
<p>Supplementary material to accompany: van den Berg, E. J., Williams, J. N.*, Stirling, M. W., Barrell, D. J. A., Griffin, J. D., Litchfield, N. J., & Wang, N. (2024). Late Quaternary activity of the NW Cardrona Fault, Otago, New Zealand. <em>New Zealand Journal of Geology and Geophysics</em>, 1–21. https://doi.org/10.1080/00288306.2023.2297962</p> <p>This dataset includes:</p> <ul> <li>Supplement S1: Supplementary figures S1-S3</li> <li>Supplement S2: Code used to generate the OxCal models for the Macdonalds Creek and Gibbston trenches</li> </ul> <p>*Corresponding author: jack.williams@otago.ac.nz</p>
Large surface-rupture gaps and low surface fault slip of the 2021 Mw 7.4 Maduo earthquake along a low-activity strike-slip fault, Tibetan Plateau
<p>In this data set, Text S1 describes methods of (i) field investigation, UAV image collection and interpretation and (ii) horizontal and vertical displacement measurements. Figure S3 shows pre-event topographic expressions of the Maduo earthquake fault. Tables S1 and S2 provide measurement results of horizontal and vertical displacements, respectively. Datasets 1-4 provide the UAV flight swath, interpreted surface ruptures and secondary cracks, horizontal displacements and vertical displacements.</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>
Lithospheric structure controls the sequentially active detachment faulting at the Longqi segment on the Southwest Indian Ridge
<p><strong>supplementary materials for "Lithospheric structure controls the sequentially active detachment faulting at the Longqi segment on the Southwest Indian Ridge"</strong></p>
Three-dimensional offsets of geomorphic piercing lines displaced by the quaternary-active Beaufort range fault, northern Cascadia forearc, BC, Canada
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DATA SET FOR: Active faulting, submarine surface rupture and seismic migration along the Liquiñe-Ofqui fault system, Patagonian Andes
<p>Data description: These data corresponde to high-resolution bathymetry and seismic reflection profiles obtained in the inner fjord west of Puerto Aysén (between 73.13°- 72.68°W and 45.32°-45.47°S; Figs. 1 and 2). The data set was obtained during a geophysical study as part of the DETSUFA project (Deslizamientos Tsunamigénicos en el Fiordo de Aysén; Lastras et al., 2013), which took place between March 4th and 17th, 2013, aboard the R/V BIO Hésperides.<br> <br> KONGSBERG SIMRAD multibeam EM-1002S was used to obtain bathymetric data, and it works with 111 beams at a 96 kHz sonar frequency and with a maximum ping rate of >10 Hz. Equidistant mode was used for swath bathymetry acquisition. This array maximized the number of beams facilitating data acquisition and obtaining a homogenized final grid with improved resolution, with tracks separated every 150 m. The swath thickness was the same regardless of width, generating a 50% overlap between each track, with the exception of areas located near the coast. Expendable Bathythermograph (XBT) probes were used at specific sites to measure changes in water sound velocity due to eventual changes in fresh water circulation, tides, and sediment.<br> <br> Seismic reflection data were acquired using an array of two BOLT air guns (165 and 175 inches3), which were towed behind the vessel stern. The configuration used in the seismic sources was 2,000 psi, a depth of 3 m for the gun, with a firing rate of 15 m over the seafloor. A 100 m long mini-streamer with a 25 m active section, corresponding to one single channel, recovered the shots. The seismic data were recorded by using the DELPH SEISMICPLUS system with a recording length of 4.0 s and a preamplifier gain of 8 Hz. The raw seismic data were processed aboard the SMT Kingdom Suite, including the navigation and standard processes of electrical noise removing (50 Hz filter), gain amplifier and bandpass filtering, to improve data visualization. Postprocessing included the migration of the sea bottom diffractions and the muting of the water column performed in Seismic-Unix.</p> <p>Files:</p> <p>Raw Seismic reflection data for lines 05, 06 and 07 (SU & SEG files)</p> <p>Masked Seismic profiles for lines 05, 06 and 07 (SU, PDF & PS files)</p> <p>Bathymetry of inner and outer Aysén Fjord (ASCII file)</p>
Displacement accumulation and sampling of paleoearthquakes on active normal faults of Crete in the eastern Mediterranean
<p>The Table S1 (uploaded separately) presents fault data and calculated values of earthquake parameters that underpin the interpretations and conclusions presented in Nicol et al. (G3, submitted June 20, 2020). The Locations of each fault are shown in Figure 2 of the main manuscript.</p> <p>In detail: The Table S1 summarises the attributes on all active faults studied on Crete. Single Event Displacement (SED), Recurrence Interval (RI), Moment Magnitude (M<sub>w</sub>) and number of paeoearthquakes have been estimated for each fault using topographic fault lengths and Quaternary displacement rates in conjunction with the Wesnousky (2008) equations (see table footnote for equations used to calculate SED and Mw). Mw calculated using Wesnously (2008) equations are unconstrained at lengths < 15.5 km. Note that the short-term (e.g. the post-glacial period) displacement rates for the Lentas (ID=43) and South Central Crete (ID=44) faults derive from time periods of c. 50 kyr and 125 kyr, respectively (from Gallen et al., 2014). Nevertheless, the estimated number of events on these two faults correspond to the post-glacial period (16.5 kyr). Table S1 presents uncertainties of +40% and -20% for maximum displacements derived from topography. Here we are conservative and have allowed larger errors for the maximum fault displacements to account for greater errors associated with erosion. The calculated earthquake recurrence interval (RI) in Table S1 derives from SED/Quaternary displacement rate.</p>
Fluvial Response to Differential Activity of the Litang Fault System: Implications for Fault Kinematics and Geodynamics in the Southeastern Tibetan Plateau
<p>Supporting information accompanies the publication "Fluvial Response to Differential Activity of the Litang Fault System: Implications for Fault Kinematics and Geodynamics in Southeastern Tibetan Plateau ". </p> <p>Table S1 presents information related to all the extracted river channels including the drainage area and outlet elevation. The elevation, horizontal and vertical migration distances of knickpoints, as well as<em> ksn</em> values above and below the knickpoints have also been included.</p> <p>All the river longitudinal profiles extracted in this study, including the knickpoint position on the longitudinal profiles and the high-precision DEM data presented in Figures 9a and 10a, are provided .</p>
What controls active faulting in Tertiary and Quaternary sequences ?
<p>Structural, geomechanical and XRD datasets obtained from outcrop investigations of Galera Fault zone located in the Guadix-Baza basin, SE Spain is presented here.</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>
Active fault surface traces of the southern Alpine Fault Zone, New Zealand
<p>This repository contains detailed, lidar-enabled geomorphic mapping of active fault surface traces assoicated with the southern Alpine Fault Zone in New Zealand.</p> <p> </p> <p> </p>
Datasets for "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", Journal of Geophysical Research: Solid Earth
<p>The following files were used in the analysis from "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", <em>Journal of Geophysical Research: Solid Earth</em>:</p> <p><strong>alos2_csk/alos2_sm1_dsc_20180504_20180713/:</strong> Includes DEM used in processing of the ALOS-2 SM1 descending interferogram spanning 4 May 2018–13 July 2018 (dem.2alks_2rlks.crop.*); geocoded SAR offsets in pixels (range resolution=1.43 m/pixel; azimuth resolution=2.01 m/pixel; denseOffsets.bil.2alks_2rlks.geo*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.2alks_2rlks.geo.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.2alks_2rlks.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.2alks_2rlks.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_sm3_asc_20180114_20180701/:</strong> Includes DEM used in processing of the ALOS-2 SM3 ascending interferogram spanning 14 January 2018–1 July 2018 (dem.crop.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_wd1_dsc_147_180518_180629/</strong>: Includes DEM used in processing of the ALOS-2 WD1 descending interferogram spanning 18 May 2018–29 June 2018 (crop.dem.*); geocoded, unwrapped interferometric phase (filt_180629-180518_2rlks_14alks.unw.geo.*) ; geocoded incidence and heading angle for the interferogram (180629-180518_2rlks_14alks.los.geo.*); geocoded coherence for the interferogram (180629-180518_2rlks_14alks.cor.geo.*); geocoded mask for the interferogram (filt_topophase.unw.masked.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180617_20180703/:</strong> Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 17 June 2018–3 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180703_20180719/</strong>: Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 3 July 2018–19 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180618_20180704/:</strong> Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 18 June 2018–4 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180704_20180720/: </strong>Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 4 July 2018–20 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>S1.zip</strong>: Unwrapped, geocoded interferometric phase in meters for Sentinel-1 ascending and descending interferograms, spanning time periods of interest. </p> <p><strong>S1_20180630_20180706_asc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 ascending interferogram spanning 30 June 2018–6 July 2018.</p> <p><strong>S1_20180701_20180707_desc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 descending interferogram spanning 1 July 2018–7 July 2018.</p> <p><strong>tandemx12m_crop.grd</strong>: TanDEM-X 12 meter DEM in meters.</p> <p><strong>pleaides_tandemx12m_diff.grd</strong>: Difference between the TanDEM-X 12 meter DEM and the Pléiades-derived DEM, computed from images on 29 October 2018 and 6 December 2019.</p> <p><strong>trapdoorFaultSlip.zip</strong>: Discretized trapdoor fault patch dip-slip modeled to fit deformation from Sentinel-1 ascending interferograms, estimated using the Classic Slip Inversion software.</p> <p><strong>trapdoorFaultTraces.zip</strong>: Caldera and trapdoor fault traces, derived from <em>Bell et al. 2021</em>.</p> <p><strong>SN14_tilt_10s_2018-19.txt</strong>: Text filt containing date-time (sampled at 10 s, in matplotlib date-time number format), N-S tilt and E-W tilt. Tilt values can be converted to microradians by multiplying by a factor of 0.00129. Tilt data obtained from authors of <em>Bell et al. 2021</em>. For use of this dataset, please cite <a href="https://doi.org/10.1038/s41467-021-21596-4">https://doi.org/10.1038/s41467-021-21596-4</a>.</p>
Data from: Thermal springs and active fault network of the central Colca River basin, Western Cordillera, Peru, published in Journal of Volcanology and Geothermal Research
<p>We used hydrogeochemical analysis of 35 water samples from springs and geysers, together with isotopic (δ<sup>18</sup>O and δD) analysis, chemical and mineral studies of precipitates collected in the field around these outflows, and field observations to study the thermal system of the Colca River basin in S Peru. We aimed to determine the geochemistry of thermal waters, identify fluid sources and their origin, estimate reservoir temperature, and discuss the regional tectonic and volcanic framework. Our findings presented in Tyc et al. (2022; https://doi.org/10.1016/j.jvolgeores.2022.107513) corroborate a heterogeneous and complex geothermal system in the central region of the Colca River basin. This system exhibits contrasting hydrogeochemical and physical characteristics, variable isotope compositions, distinct reservoir temperatures, and associated precipitates near thermal springs. The control of water chemistry in this area is closely linked to the activity of the Ampato-Sabancaya magmatic chamber and the presence of tectonic structures, which enable intricate interactions between meteoric waters, magmatic fluids, and gases.</p> <p>Here, we present datasets used in the article (Tyc et al., 2022; https://doi.org/10.1016/j.jvolgeores.2022.107513), including:</p> <p>- Physicochemical characteristics of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 1)</p> <p>- Chemical and isotopic composition of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 2) and those monitored by INGEMMET in years 2013-2018 (Table 3)</p> <p>- Chosen molecular ratios discussed in Tyc et al., 2022 (Table 4)</p> <p>- Calculated reservoir temperature with the use of different Na/K geothermometers (Table 5)</p> <p>- Mineral phases in efflorescences precipitating at the water sampling sites (Table 6).</p> <p>Thirty-five sets of water samples were collected in the field in September 2012 and August–September 2017 using polyethylene bottles of high density (Table 1). Consequently, these were analyzed in the Water Analysis Laboratory at the University of Silesia in Katowice (Poland; Table 2). Water temperatures, pH, and electrical conductivity were measured in the field using portable pH meter CP-315 and conductivity meter CC-315, both with temperature sensors, with an accuracy of ±0.1 °C, ±0.01 pH, and ± 0.1% (up to 19.999 mS/cm) or ± 0.25% (above 20.00 mS/cm), respectively. Discharge of springs was estimated if possible (Table 1). Both cations and anions were analyzed by ion chromatography using Methron 850 Professional Ion Chromatograph with separate Metrosept C4–150 and A-supp 7–250 columns for cations and anions, respectively (Tables 2 and 4). Analysis of water analyses collected by INGEMMET in years 2013-2018 was performed at the INGEMMET Chemical Laboratory in Lima with the use of ion chromatography (Dionex ICS 5000) for the determination of anions and inductively coupled plasma optical emission spectrometry (ICP-OES) – VARIAN for cations (Table 3). Isotopic analyses (δ<sup>2</sup>H, δ<sup>18</sup>O) of 17 water samples collected in 2017 were performed at the Stable Isotope Laboratory Institute of Geological Sciences Polish Academy of Sciences (Table 2). The δ<sup>2</sup>H values of studied H<sub>2</sub>O were measured using the H-Device peripheral coupled to MAT 253 IRMS (Thermo Scientific) in a dual inlet system. For the determination of δ<sup>18</sup>O in H<sub>2</sub>O, an equilibration technique was used. The analysis used the GasBench II peripheral device (Thermo Scientific) coupled to MAT 253 IRMS with a continuous He flow. The AquaChem 4.0.284 software was used to evaluate the water samples' geochemical properties and calculate reservoir temperature for thermal waters (Table 5). Precipitates found at the water sampling sites were collected separately into plastic bags with strings and sealed boxes. These samples were subsequently analyzed at the Institute of Earth Sciences, University of Silesia in Katowice. The qualitative chemical composition and mineral characteristics were examined using a Philips XL 30 ESEM/TMP scanning electron microscope coupled with an energy-dispersive spectrometer (EDS; EDAX type Sapphire). The phase composition of the precipitates was determined through X-ray diffraction (XRD) using a Philips PW 3710 diffractometer. The XRD data were analyzed and interpreted using the X'Pert HIGHScore Plus software (Table 6).</p>
Offshore active faults associated with the 2020 Masbate earthquake investigation
<p>This dataset includes the vector files of the offshore active fault traces mapped using acoustic sub-bottom profiling and interpretation of archived seismic profiles. The acoustic sub-bottom profiling survey was conducted in March and June 2021. The archived seismic profiles were acquired from the Department of Energy (DOE) of the Philippine government. </p>
Online tree-based planning for active spacecraft fault estimation and collision avoidance
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Dataset for Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera). Tectonics
<p>Dataset for "Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera)" Tectonics<br><br>File "hypoDD_10_300.reloc.txt" compile all the relocated seismicity in the study area.<br>Files "810_.NEU, 811_.NEU, 812_.NEU, 813_.NEU, 814_.NEU, 815_.NEU, and 816_.NEU" presents the time series data of GNSS sites of the Zafarraya network.</p>
Foreshock Activity Promoted by Locally Elevated Loading Rate on a 4-meter-long Laboratory Fault
<p>Experimental data and event catalog used for the study "Foreshock Activity Promoted by Locally Elevated Loading Rate on a 4-meter-long Laboratory Fault"</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>
flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau
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