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462 results for “Volcano”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth
<p>Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file.</p> <p>Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, <a href="https://doi.org/10.1029/2021JB021910">https://doi.org/10.1029/2021JB021910</a>).</p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability.</p> <p>Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0044">2000</a>) are available at <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a>, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0036">2021</a>; <a href="https://doi.org/10.5281/zenodo.4558121">https://doi.org/10.5281/zenodo.4558121</a>).</p> <p> </p> <p>Dataset column key:</p> <p>time = Origin time of seismic event (UTC)</p> <p>lat = Hypocentre latitude in decimal degrees</p> <p>lon = Hypocentre longitude in decimal degrees</p> <p>depth = Hypocentre depth in km</p> <p>rms = RMS error for phase arrival picks and hypocentre (sec)</p> <p>erh = Estimate of horizontal Gaussian error (km)</p> <p>erz = Estimate of vertical Gaussian error (km)</p> <p>azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre)</p> <p>cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: <a href="https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd">https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd</a>)</p> <p>nab*_p_time = P-wave arrival time for station NAB* (UTC)</p> <p>nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1)</p> <p>nab*_s_time = S-wave arrival time for station NAB* (UTC)</p> <p>nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)</p> <p> </p> <p>Station csv column key:</p> <p>Network = Seismic network name</p> <p>Station = Seismic station name</p> <p>Latitude = Latitude in decimal degrees</p> <p>Longitude = Longitude in decimal degrees</p> <p>Elevation_asl_km = Station elevation in km above sea level</p>
VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"
<p>This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article:</p> <p>"<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>",</p> <p>published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which </p> <p>have made possible the monitoring of Deception Island since early 1990s.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Digital Elevation Models of Hunga Volcano; pre- and post- 15 January 2022 eruption
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex. The first is a pre-2022 eruption elevation model. The second is a post-2022 eruption elevation model.</p><p>Hunga Volcano is a volcanic complex near the island of Tongatapu in the Kingdom of Tonga. The volcano rises from ~2,500 m depth, a caldera at its summit, and two islands, Hunga Tonga and Hunga-Ha'apai, at the on the rim of the caldera. An eruption during December 2014-January 2015 was centered between the islands and combined them into one larger structure named Hunga Tonga – Hunga Ha'apai (HTHH). HTHH erupted violently on 15th January 2022, sending large clouds of ash into the atmosphere, triggering a tsunami, and reducing the size of the islands of Hunga Tonga and Hunga Ha'apai.</p><p>As a result of this event, the NIWA-Nippon Foundation Tonga Eruption Seabed Mapping Project (TESMaP) is a multidisciplinary research plan involving geological, oceanographic and biological studies that centered around three objectives:</p><ol><li>To determine the impacts of volcanic ash on ocean productivity, species composition, and biogeochemical cycling in the water column.</li><li>To determine the immediate nature and extent of the impact of ash fall/turbidity flows on deep-sea sediments and benthic ecosystems.</li><li>To determine the recovery potential of the deep-sea ecosystem.</li></ol><p>This project involved two survey voyages of the volcano and its surrounding waters. The first was carried out from <i>RV Tangaroa</i> in April and May 2022 (Mackay et al., 2022) and the second was carried out by the <i>USV Maxlimer</i> in August 2022.</p><p>TESMaP was funded from a combination of sources including The Nippon Foundation, Japan; the Natural Environmental Research Council, UK, Japan Agency for Marine Earth Science and Technology, the Tangaroa Reference Group (TRG) for ship time and the NIWA Oceans Centre. Support was given by The Nippon Foundation Seabed 2030 project and by GEBCO Alumni.</p>
Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface
<p>Video-documentation of the <strong><em><a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a></em> Volcano-Independent Seismic Recognition (VI.VSR) software</strong>, supported by the <a href="https://cordis.europa.eu/project/id/749249"><strong><em>VULCAN.ears</em></strong></a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the <em>"Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - </em>(Cortés et al., Frontiers in Earth Sciences, 2021) article. <em><strong>VI.VSR aim</strong></em> is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios.</p> <p>The material includes 2 VDs:</p> <ol> <li><em>"VI.VSR+geoStudio_intro.mp4"</em> -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting <em>geoStudio</em> and its role in the whole <em>VULCAN.ears</em> platform.</li> <li><em>"VI.VSR.by.geoStudio_case.studies.mp4"</em> -> running the VI.VSR case studies presented in the <em>(Cortés et al., 2021)</em> manuscript.</li> </ol> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
NAPLES (moNitoring mAps of camPania voLcanoES)
<p>NAPLES is an open access database. You can view and download the Vesuvius Observatory monitoring networks maps, grouped by monitoring network and geographic areas. The active networks are:<br>• Seismic Network<br> o Permanent Seismic Network<br> o Mobile Seismic Network<br>• Geodetic Network<br> o GPS network<br> o Tiltmetric network<br> o Mareographic Network<br> o Altimetric Network<br> o Gravimetric Network<br> • Volcanological Network<br> o Permanent Network of Thermal Cameras<br> o Periodic Campaigns - Thermal Monitoring with Mobile Thermal Camera and Thermocouple<br>• Geochemical Network<br> o Permanent Geochemical Network<br> o Periodic Campaigns</p>
Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting: Supplementary Model Files
<p>Input and output model files for the manual script titlted "Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting" submitted to Journal of Geophysical Research: Solid Earth.</p>
3D FEM-based inverse model of Nevado del Ruiz - St. Isabel volcanoes (Colombia)
<p><strong>Description of model and data</strong></p> <p>The files include a FEM-based inverse model for the optimization of parameters of a pressure source responsible for surface deformation. The investigated source parameters are the position of the source center, the three semi-axis, the source strike orientation, the source dip orientation, and the source overpressure. The observations used for the inversion are ascending and descending ground velocities. The optimization is based on Least-Squares objectives using the Monte Carlo method. The file of observations needed for the computation of the Least-Squares objectives (to be uploaded in the optimization node) requires four columns (x,y,z, velocities. All in meters, UTM coordinates-UTM zone 18N, and comma-separated).</p> <p>The model takes into consideration the heterogeneous distribution of material elastic properties. The model does not provide the files for the observations and material properties (at the link: https://zenodo.org/record/5575972), but the structure for the optimization model in which new files can be uploaded for a customized model.</p> <p>The model includes the compensation for the stresses induced by the topography (edifices’ load). The file for the construction of the topographic surface is included as a .txt file (the position x,y of the points is in UTM coordinates-UTM zone 18N, the altitude z is in meters). The far-field is modeled as a hemisphere and it is located at 35 km from the center of the model, which is between the Nevado del Ruiz volcano and Santa Isabel volcano.</p> <p>The model is built with Comsol Multiphysics v 5.6 using the modules Optimization and Structural Mechanics modules, and it is provided as a Comsol .mph file.</p> <p> </p> <p>Datasets and model are results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 79381</p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 46 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Volcanic Lightning and Continual Radio Frequency Impulses at Sakurajima Volcano: A Multiparametric Dataset
<p>This is a multiparametric data set of volcanic activity at Sakurajima volcano in Japan. The data set was collected in May and June 2015. The data set includes the following types of data: Lightning Mapping Array data, slow and fast electric field waveforms, log-RF VHF data, infrasound data, plume height, velocity, and temperature data.</p>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
Constraints on the emplacement of Martian nakhlite igneous rocks and their source volcano from advanced micro-petrofabric analysis
<p>Martian nakhlite meteorite electron backscatter data and magma body unit thickness calculation code.</p>
High precision photogrammetry data of Lascar Volcano acquired by UAS survey in 2017 and 2020
<p>Here we present a high precision photogrammetry dataset of Lascar summit crater, which acquired by unmanned aircraft system (UAS) in Nov 2017 and Feb 2020 respectively, and reconstructed by Structure-from-Motion (SfM) method. In which, optical orthomosaic and DEM (Digital Elevation Model) were processed in Agisoft Metashape (version 1.7.3), preprocessing of thermal data was conducted in Thermoviewer (v3.0.7) and thermal mosaic was generated by Pix4Dmapper (v4.5.6). All data was projected to global coordinates (WGS 1984 UTM Zone 19 South). Thermal orthomosaic was georeferenced to 2020 orthomosaic in ArcMap (version 10.8). Employed UAS and SfM-derived product are given as follows:</p> <p>(1) 2017 orthomosaic (7.7 cm/pix) and DEM (15.6 cm/pix): DJI Mavic Pro Platinum </p> <p>(2) 2020 orthomosaic (7.0 cm/pix) and DEM (13.7 cm/pix): DJI Phantom 4 RTK</p> <p>(3) 2020 additional orthomosaic (5.3 cm/pix): DJI Mavic 2</p> <p>(4) 2020 thermal orthomosaic (spatial resolution: 45.0 cm/pix, radiometric resolution: 0.04 degree/pix): FLIR Tau 2 640 attached to DJI Phantom 4 RTK</p>
Self-supervised learning of seismological data reveals undocumented eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
<p>The following files are shared:<br> - The scripts used to train the model and generate the figures of the article<br> - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)<br> - The clusters organization with their associated images</p>
Data for "Volcano-tectonic interactions at Sabancaya volcano, Peru: Eruptions, magmatic inflation, moderate earthquakes, and fault creep"
<p>Data and models presented in the paper "Volcano-tectonic interactions at Sabancaya volcano, Peru: Eruptions, magmatic inflation, moderate earthquakes, and fault creep". See file "README.txt" for detailed descriptions of each item.</p>
ERT data collected at the Corona volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2017
<p>This dataset contains the ERT (Electrical Resistivity Tomography) data collected between 22 and 23 November 2017 at the Corona volcano (Lanzarote, Canary Islands, Fig. 1) for the detection of lava tubes and the stratigraphic investigation of planetary volcanic analogues. This geophysical survey was carried out within the European Space Agency (ESA) testing campaign PANGAEA-X 2017 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Two ERT profiles were acquired in NE-SW and NNE-SSW orientations (Fig. 1). These were located roughly orthogonal to the Corona lava tube system and as far as possible on top of the main lava tube axes. The longer profile, profile D, is 470 m in length and was obtained using 48 electrodes spaced 10 m apart. The profile orientation is from SW to NE (electrode 1 to 48). The profile was acquired to detect lava tubes in test site D (sub-area south) where the exact location of a lava tube was known thanks to a LiDAR TLS (Terrestrial Laser Scan) subsurface survey (Santagata et al., 2018). A shorter profile, profile E, is 235 m long and was obtained using 48 electrodes 5 m apart. The profile orientation is from SSW to NNE (electrode 1 to 48). This profile was acquired in test site E (sub-area north) to provide a more detailed investigation of the potential existence of inaccessible sections of the tube whose location could be indicated by the evidence of closely-spaced aligned collapse structures.</p> <p>Each profile was collected using measure sequences compounded by 276 Wenner-Schlumberger array quadrupoles which ensure high vertical resolution and signal amplitude and 328 dipole-dipole array quadrupoles which provide enhanced lateral resolution. A fully automatic multi-electrode resistivity meter SYSCAL Jr Switch-48 by IRIS Instruments (400 V max output voltage, 1200 mA max output current, 100 W max output power, <a href="http://www.iris-instruments.com/syscal-juniorsw.html">http://www.iris-instruments.com/syscal-juniorsw.html</a>), was used for data collection.</p> <p>At most of the measurement points, it was necessary to drill the basalt using a hand drilling machine in order to place the tips of the electrodes into the ground at a depth of approximately 40 cm. The electrodes also needed to kept moist to reduce contact resistance between the electrode and the ground. A large amount of water (up to 2 liters per point) was needed for profile D, situated in an area above the lava tubes with very porous dry soil cover.</p> <p>The dataset is presented as a spreadsheet format which has the "space" as separator and the ".txt" extension. The structure of such a file is the following one:</p> <p>#, El array, Spa1/4, Rho, Dev, M, Sp, Vp, In, Time, Spa5/12, M1/20</p> <p>- #: Data point number</p> <p>- El array: Electrode array</p> <p>- Spa. 1/4: four spacing parameters (corresponding to the electrode array – in m)</p> <p>- Rho: resistivity value (in Ohm.m)</p> <p>- Dev: standard deviation (quality factor, in %)</p> <p>- M: global chargeability value (induced polarization parameter (in mV/V – "=0" if only-resistivity data))</p> <p>- Sp: spontaneous polarization (measured just before the injection, in mV)</p> <p>- Vp: measured primary voltage (in mV)</p> <p>- In: injected current intensity (in mA)</p> <p>- Time: injection time (pulse duration, in s)</p> <p>- Spa. 5/8: other spacing parameters (in m)</p> <p>- Spa. 9/12: electrode elevation (in m)</p> <p>- M1/M20: partial chargeability values (induced polarization window (in mV/V – "=0" if only-resistivity data))</p> <p> </p> <p>Acknowledgements</p> <p>The authors are grateful to ESA and all PANGAEA-X 2017 staff, particularly Loredana Bessone, Matthias Maurer, Herve Stevenin and Igor Drozdovskiy for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. Regional and local remote sensing data were obtained by the Spanish Instituto Geográfico Nacional (https://www.ign.es) and Gobierno de Canarias (https://www.grafcan.es, <a href="https://opendata.sitcan.es/">https://opendata.sitcan.es</a>).</p> <p> </p> <p>References</p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Santagata, T., Sauro, F., Massironi, M., Pozzobon, R., Del Vecchio, U., Lazzaroni, M., Damiano, N., Tonello, M., Tomasi, I., Martínez-Frìas, J. and Mateo Medero, E., 2018. Subsurface laser scanning and photogrammetry in the Corona Lava Tube System, Lanzarote, Spain, EGU General Assembly 2018, pp. EGU2018-5290.</p>
Figs 126–137 in Revision of Trechus Clairville, 1806 of the Bale Mountains and adjacent volcanos, Ethiopia (Coleoptera, Carabidae, Trechini)
Figs 126–137. Trechus spp., aedeagal median lobe, left lateral view (126, 128–129, 131, 133, 135–137) and dorsal view (127, 130, 132, 134). 126–127. T. sanettii sp. nov., paratypes. 128. T. angavoensis sp. nov., holotype. 129–130. T. batuensis Magrini & Sciaky, 2006, specimens from Wasama Valley. 131–132. T. abalkhasimi sp. nov., paratypes. 133–134. T. fisehai sp. nov., paratypes. 135. T. tragelaphus sp. nov., holotype. 136. T. nigrifemoralis sp. nov., holotype. 137. T. balesilvestris sp. nov., holotype.
Figs 115–125 in Revision of Trechus Clairville, 1806 of the Bale Mountains and adjacent volcanos, Ethiopia (Coleoptera, Carabidae, Trechini)
Figs 115–125. Trechus spp., aedeagal median lobe, left lateral view (115–116, 118, 120, 122, 124–125) and dorsal view (117, 119, 121, 123). 115. T. depressipennis sp. nov., holotype. 116–117. T. mekbibi sp. nov., paratypes. 118–119. T. hagenia sp. nov., paratypes. 120–121. T. colobus sp. nov., paratypes. 122–123. T. wiersbowskyi sp. nov., paratypes. 124. T. haggei sp. nov., paratype. 125. T. grandipennis sp. nov., holotype.
Figs 111–114. Trechus spp., elytra. 111. T in Revision of Trechus Clairville, 1806 of the Bale Mountains and adjacent volcanos, Ethiopia (Coleoptera, Carabidae, Trechini)
Figs 111–114. Trechus spp., elytra. 111. T. haggei sp. nov., paratype, ³. 112. T. tragelaphus sp. nov., holotype. 113. T. nigrifemoralis, sp. nov., holotype. 114. T. balesilvestris, sp. nov., holotype. The arrows point to the insertions of the discal setae and the preapical seta.
Figs 99–102. Trechus spp., elytra. 99. T in Revision of Trechus Clairville, 1806 of the Bale Mountains and adjacent volcanos, Ethiopia (Coleoptera, Carabidae, Trechini)
Figs 99–102. Trechus spp., elytra. 99. T. fisehai sp. nov., paratype, ³. 100. T. angavoensis sp. nov., holotype. 101. T. batuensis Magrini & Sciaky, 2006, ³ from Sanetti Plateau near Tulo Dimptu. 102. T. batuensis, ³ from Tegona Valley. The arrows point to the insertions of the discal setae and the preapical seta.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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