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53 results for “volcanic eruption”
A successful short-term volcanic eruption forecasting using seismic features: datasets and Sotware
<p>Successful Short-Term Volcanic Eruption Forecasting Using Seismic Features, Suplementary Material</p> <p>by Rey-Devesa (1,2), Benítez (3), Prudencio, Ligdamis Gutiérrez (1,2), Cortés (1,2), Titos (3), Koulakov (4,5), Zuccarello (6) and Ibáñez (1,2).</p> <p><br> Institutions associated:</p> <p>(1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.</p> <p>(2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.</p> <p>(3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain.</p> <p>(4) Trofimuk Institute of Petroleum Geology and Geophysics SB RAS, Prospekt Koptyuga, 3, 630090 Novosibirsk, Russia</p> <p>(5) Institute of the Earth’s Crust SB RAS, Lermontova 128, Irkutsk, Russia</p> <p>(6) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa (INGV-Pisa), via Cesare Battisti, 53, 56125, Pisa, Italy.</p> <p><br> Acknowledgment:</p> <p>This study was partially supported by the Spanish FEMALE project (PID2019-106260GB-I00).<br> P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN),<br> Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE),<br> and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719).<br> Ivan Koulakov was supported by the Russian Science Foundation (Grant No. 20-17-00075).<br> Luciano Zuccarello was supported by the INGV Pianeta Dinamico 2021 Tema 8 SOME project (grant no. CUP D53J1900017001)<br> funded by the Italian Ministry of University and Research<br> “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018”.<br> English language editing was performed by Tornillo Scientific, UK.</p> <p><br> Data availability statement:</p> <p>1.- Seismic data from Kilauea, Augustine, Bezymianny (2007), and Mount St. Helens are available from the IRIS data repository (http://ds.iris.edu/seismon/index.phtml).<br> (An example of the Python code to access the data is described below.)<br> 2.- Seismic data from Bezymianny (2017-2018) are available from Ivan Koulakov (ivan.science@gmail.com) upon request.<br> 3.- Seismic data from Mt. Etna are available from INGV-Italy upon request (http://terremoti.ingv.it/en/help),<br> also available from the Zenodo data repository (https://doi.org/10.5281/zenodo.6849621).</p> <p> </p> <p>Access code in Python to download the records of Kilauea, Augustine and Mount St. Helens volcanoes, from the IRIS data repository.</p> <p>'''To access the raw signals please first install ObsPy and then execute following commands in a python console: '''</p> <p>Example:</p> <p>from obspy.core import UTCDateTime<br> from obspy.clients.fdsn import Client<br> import obspy.io.mseed<br> client = Client('IRIS')<br> t1 = UTCDateTime('2006-01-10T00:00:00')<br> t2 = UTCDateTime('2006-01-12T00:00:00')<br> raw_data = client.get_waveforms(<br> network='AV',<br> station='AUH',<br> location='',<br> channel='HHZ',<br> starttime=t1,<br> endtime=t2)</p> <p>'''To further download station information execute: '''</p> <p>xml = client.get_stations(network='AV',station='AUH', <br> channel='HHZ',starttime=t1,endtime=t2,level='response')</p> <p>''' 'To scale the data using the station’s meta-data: '''</p> <p>data = raw_data.remove_response(inventory=xml)</p> <p>''' To filter, trim and plot the data execute: '''</p> <p>data.write("Augustine.mseed", format="MSEED")</p> <p>data.filter('bandpass',freqmin=1.0,freqmax=20)<br> data.trim(t1+60,t2-60)<br> data.plot()</p> <p>Contents:</p> <p>6 different Matlab codes. The principal code is called FeatureExtraction.<br> The codes rsac.m and ReadMSEEDFast.m are for reading different format of data. (Not developed by the group)<br> Seismic Data from Mt. Etna for using as an example.</p> <p> </p>
Improving Volcanic SO2 Cloud Modeling Through Data Fusion and Trajectory Analysis: A Case Study of 2022 Hunga Tonga Eruption
<p><strong>Dataset Overview</strong>: This dataset comprises approximately 500 clusters of aggregated observational data collected from January 16 to 20 during the ascending (ASC) and descending (DES) periods. We grouped a large number of observation points into these clusters and calculated trajectories from the center of each cluster. The choice of 500 clusters was driven by pragmatic considerations, aiming for a balance between computational feasibility and the level of detail needed for our analysis.</p> <p><strong>Data Unit Description</strong>: The "mass" values in this dataset for each cluster are calculated by multiplying the mass per unit area (<span><span>g/m2</span></span>) of individual data points by the area covered by each point, thus providing the total mass in grams (g). The "heights" are presented in units of kilometers (km), representing the observed top heights of each cluster.</p>
Data for "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023
<p>Data used for figures in "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023</p> <p>See https://github.com/awells96/Raikoke for code to reproduce the figures.</p>
Differences in volcanic risk perception among Goma's population before the Nyiragongo eruption of May 2021, Virunga volcanic province (DR Congo)
<p>A short presentation of a study published focussing on the Differences in volcanic risk perception among Goma’s population before the Nyiragongo eruption of May 2021, Virunga volcanic province (DR Congo).</p>
Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption: Misfit plots in model parameter space
<p>Supporting documents for the correction of the publication <em>Multi‐Event Explosive Seismic Source for the 2022 Mw 6.3 Hunga Tonga Submarine Volcanic Eruption, </em>published in The Seismic Record (<a href="https://doi.org/10.1785/0320220027">https://doi.org/10.1785/0320220027</a>).</p>
Zenodo Collection: Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption
<p>This Zenodo collection contains figures and results from grid searches used to estimate point source parameters (force or moment tensor) for the main seismic subevent of the 2022 Hunga-Tonga submarine volcanic eruption. The collection includes misfit maps and waveform fits for the best-fitting force or moment tensor, as well as MTUQ weight files and a zipped version of the MTUQ code. These results were obtained using various software tools, including MTUQ, Axisem, Instaseis, Syngine, Obspy, GMT, and PyGMT. The collection was prepared for a manuscript in review for Geophysical Journal International.</p>
IDW interpolation dataset of ash and tephra deposition following the 2021 Tajogaite volcanic eruption on La Palma, Canary Islands, Spain
<p>The compiled dataset is the result of a field collection campaign to measure the depth of the ash and tephra layer in the aftermath of the 2021 volcanic eruption (Tajogaite) on the island of La Palma, Canary Islands, Spain.</p> <p>The dataset consists of two files: a shapefile and a GeoTIFF raster. The shapefile is a point layer file that displays the location of all ash depth measurements (415 points) taken in the field. To improve our sampling near the crater, where safety and time constraints prevented field collection, we manually sampled additional drone-based measurements (66 points). We combined these data into a single dataset ("ash_depth"; 481 total points) that was then used as input for a spatial interpolation using Inverse Distance Weighting (IDW).</p> <p>The IDW interpolation was performed using the Spatial Analyst toolbox in ArcMap 10.8.1 (Esri, 2021). As an exact deterministic interpolation, IDW estimates pixels values of unknown points by using average distance and a weight between sample points (Watson & Philip, 1985). This is ideal for a dataset that includes many field measurements since IDW interpolates between the minimum and maximum of the collected data. The model parameters were adjusted manually but the best results were yielded using the default settings, with the exception of the output cell size. The output cell size was calibrated to 2 m. The IDW parameters can be viewed in Table 1.</p> <p>The sample point locations were resampled from the raster file to estimate the Root Square Mean Error (RMSE) and were saved to the shapefile as "ash_idw". The RMSE of the dataset is 0.34 m. For further inquiries please contact Christopher Shatto (email: christopher.shatto@uni-bayreuth.de).</p> <p> </p> <p>Please cite the data paper link to this repository as: </p> <p><strong>C. Shatto, F. Weiser and A. Walentowitz et al., Volcanic tephra deposition dataset based on interpolated field measurements following the 2021 Tajogaite Eruption on La Palma, Canary Islands, Spain, Data in Brief, https://doi.org/10.1016/j.dib.2023.109949</strong></p> <p> </p> <table> <tbody> <tr> <td>Power</td> <td>2</td> </tr> <tr> <td>Output cell size</td> <td>2</td> </tr> <tr> <td>Search neighborhood type</td> <td>Standard (circular)</td> </tr> <tr> <td>Major/minor semiaxis</td> <td>12161.79/12161.79</td> </tr> <tr> <td>Max/minimum neighbors</td> <td>15/10</td> </tr> <tr> <td>Sector type</td> <td>1</td> </tr> <tr> <td>Angle</td> <td>0</td> </tr> <tr> <td>Weight field</td> <td>None</td> </tr> </tbody> </table> <p> </p>
Pollen data: Influences of sea level changes and volcanic eruptions on Holocene vegetation in Tonga
Open the record for dataset details and reuse information.
Data from: Litter microbial and soil faunal communities stimulated in the wake of a volcanic eruption in a semi-arid woodland in Patagonia, Argentina
Large-scale disturbances can be important components of the temporal landscape of natural ecosystems, but generalities regarding ecosystem impacts are difficult due to their infrequent and unpredictable nature. Volcanic eruptions figure as one of the most prominent of these natural disturbances, but the effects on microbes and ground-dwelling arthropods, which modulate carbon and nutrient turnover, are relatively unknown. We evaluated the effects of the 2011 Puyehue-Cordón Caulle eruption in Patagonia, Argentina, on the litter and soil microbial and faunal communities in natural and afforested semi-arid ecosystems located 70 km west of the epicentre of the eruption. We hypothesized that volcanic ash deposition would strongly reduce soil faunal and microbial communities due to insecticidal effects of ash on arthropods, with a concomitant reduction in ecosystem processes. Our objective was to quantify the impact of the volcanic eruption by comparing pre- and post-eruption time points in the same study site, with nearly identical field methodology. We measured environmental variables of soil and litter moisture, pH, microbial biomass, and soil and litter microbial enzymatic activity. We evaluated ground-dwelling arthropods and nematodes using pitfall traps and soil extraction, respectively. Additionally, a parallel, controlled-condition experiment of simulated ash deposition was conducted to evaluate ash effects on litter decomposition and enzymatic activity. In the field, post-eruption soils had lower soil water content, pH and soil organic matter. Additionally, nematode abundance and soil microbial enzyme activity were significantly reduced. In contrast, ground-dwelling arthropods and litter enzymatic activity increased significantly. Finally, with simulated ash deposition, litter decomposition increased fourfold for native litter decomposition. Large-scale disturbances may play a key role in biogeochemical cycling in affected natural ecosystems, but not necessarily due to their catastrophic effects. In contrast to our original predictions, we observed a marked stimulation of biotic activity and carbon turnover in the aftermath of the Puyehue volcanic eruption, which demonstrates that the biotic component of these ecosystems has a substantial capacity to respond to these disturbances in short time frames. These results can contribute to placing the role of these large-scale infrequent disturbances in a more robust ecological context.
Case Study Scenarios (CSS) 4 for Volcanic Eruption
Open the record for dataset details and reuse information.
Volcanic plume recorded during the eruption of Eyjafjallajökull (Iceland) in 4 May 2010
<p>This repositery contains a video showing the volcanic plume associated with the eruption of Eyjafjallajökull (Iceland) on 4 May 2010 (Starting at 12:49:21 GMT). The video was recorded from a position 7.7 km south of the vent (0568182E, 7047683N) using a Sony HD-SR8e camera operating at 25 fps and at a resolution of 1440 × 810 px. </p>
The effect of the Hunga-Tonga volcanic eruption on groundwater levels in China
<p>"Barometric" folder - Barometric observed data</p> <p>"Water level" folder – Water level observed data</p> <p>pressSites.xlsx: basic information of observed station</p> <p>codes in code folder</p>
La Soufriere volcanic eruptions launched gravity waves into Space
<p>The GNSS filtered TEC observations with bandpass filtering of 10-30 min for the study of "<strong>La Soufriere volcanic eruptions launched gravity waves into Space" </strong>by Yue et al.</p>
Numerical simulation "Numerical simulation of atmospheric Lamb waves generated by the 2022 Hunga-Tonga volcanic eruption"
<p>Numerical simulation results for the atmospheric Lamb waves generated by the Hunga-Tonga volcano explosion on January 15th 2022.</p>
Dataset for "Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro" published in GeoHealth
<p>Data Repository for:</p> <p><strong>"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response <em>in-vitro" </em></strong>published in GeoHealth.<br> </p> <p>Julia Eychenne<sup>1,2*</sup>, Lucia Gurioli<sup>1</sup>, David Damby<sup>3</sup>, Corinne Belville², Federica Schiavi<sup>1</sup>, Geoffroy Marceau<sup>2,4</sup>, Claire Szczepaniak<sup>5</sup>, Christelle Blavignac<sup>5</sup>, Mickael Laumonier<sup>1</sup>, Emmanuel Gardés<sup>1</sup>, Jean-Luc Le Pennec<sup>6,7</sup>, Jean-Marie Nedelec<sup>8</sup>, Loïc Blanchon², Vincent Sapin<sup>2,4 </sup></p> <p><sup>1</sup> Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</p> <p><sup>2</sup> Université Clermont Auvergne, CNRS, INSERM, Institut de Génétique Reproduction et Développement, F-63000 Clermont-Ferrand, France</p> <p><sup>3</sup> U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA</p> <p><sup>4</sup> Biochemistry and Molecular Genetic Department, University Hospital, F-63000 Clermont-Ferrand, France</p> <p><sup>5</sup> Université Clermont Auvergne, UCA PARTNER, Centre Imagerie Cellulaire Santé, F-63000 Clermont-Ferrand, France</p> <p><sup>6</sup> Geo-Ocean, CNRS, Ifremer, UMR6538, F-29280 Plouzané, France</p> <p><sup>7</sup> IRD Office for Indonesia & Timor Leste, Jalan Kemang Raya n°4, Jakarta 12730, Indonesia</p> <p><sup>8</sup> Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ICCFn, F-63000 Clermont-Ferrand, France</p> <p><strong>This repository includes the grainsize distributions of the individual tephra fall samples, the grainsize distribution of the respirable ash sample isolated from F2, the Raman point counting data and individual spectra, the SEM images and EDX maps of the respirable ash sample, the SEM and TEM images of the <em>in-vitro</em> experiments, and the data from the LDH assays, multiplex immunoassays and RT-qPCR.</strong></p>
Fig. 3 Neochromadora aff. poecilosoma, a in Nematode succession at deep-sea hydrothermal vents after a recent volcanic eruption with the description of two dominant species
Fig. 3 Neochromadora aff. poecilosoma, a female, specimen no.7, reproductive system; b female, no. 7 total view; c male, no. 1, cuticle on the level of the pharynx; d male, no. 1, cuticle on the level of the midbody; e male, no.1, cuticle on the level of the tail. Scale in μm
Fig. 2 Neochromadora aff. poecilosoma, a in Nematode succession at deep-sea hydrothermal vents after a recent volcanic eruption with the description of two dominant species
Fig. 2 Neochromadora aff. poecilosoma, a male, specimen no.1, head region; b male, specimen no. 2; c male, no.1, anterior end; d female, specimen no. 6; e male, no. 1, posterior end; f male, no.1, spicule. Scale in μm
Fig. 1 in Nematode succession at deep-sea hydrothermal vents after a recent volcanic eruption with the description of two dominant species
Fig. 1 Examples of habitat types analyzed in this study. a Newly established active vent site colonized by the tubeworm foundation species Tevnia jerichonana and a few large Riftia pachyptila tubeworms at P-Vent in 2007. b Old inactive vent at the site East Wall showing empty tubeworm tubes and mussel shells in 2007. c Newly bare basalt adjacent to the vent site Tica in 2007. In each habitat artificial settlement devices (referred to as "sponge;" see Fig. 1c) were deployed
In-plume and out-of-plume analysis of aerosol-cloud interactions derived from the 2014-15 Holuhraun volcanic eruption
<p>This dataset contains simplified data and code to reproduce the main figures in "In-plume and out-of-plume analysis of aerosol-cloud interactions derived from the 2014-15 Holuhraun volcanic eruption", accepted for publication in Atmospheric Chemistry and Physics, 2024, same authors.</p>
Dataset of Waves generated by discrete and sustained gas eruptions with implications for submarine volcanic tsunamis
<p>This dataset contains all the data used to plot figures in the manuscript "Waves generated by discrete and sustained gas eruptions with implications for submarine volcanic tsunamis" by Yaxiong Shen, Colin Whittaker, Emily Lane, James White, William Power and Bruce Melville submitted to Geophysical Research Letters.</p> <p>The folder "jetplumefountainquantification" contains data for figure 1.</p> <p>The wavedata.xlsx contains wave data and plume rise velocity data. </p> <p>The MATLAB script fig1.m is used to visualise the jet-plume-fountain evolution.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.