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16 results for “Degassing”
Periodic Degassing Rhythms in Three Mineral Springs in the Neuwied Basin, Germany 2016
We present a geochemical dataset acquired during continual sampling over 7 months (bi-weekly) and 4 weeks (every 8 hours) in the Neuwied Basin, a part of the East Eifel Volcanic Field (EEVF, Germany). We used a combination of geochemical, geophysical, and statistical methods to describe and identify potential causal processes underlying the correlations of degassing patterns of CO2, He, Rn, and tectonic processes in three investigated mineral springs (Nette, Kärlich and Kobern). We provide for the first time, temporal analyses of periodic degassing patterns (1 day and 2-6 days) in springs. The temporal fluctuations in cyclic behavior of 4–5 days that we recorded had not been observed previously but may be attributed to a fundamental change in either gas source processes, subsequent gas transport to the surface, or the influence of volcano-tectonic earthquakes. Periods observed at 10 and 15 days may be related to discharge pulses of magma in the same periodic rhythm. We report the potential hint that deep low-frequency (DLF) earthquakes might actively modulate degassing. Temporal analyses of the CO2-He and CO2-Rn couples indicate that all springs are interlinked by previously unknown fault systems. The volcanic activity in the EEVF is dormant but not extinct. To understand and monitor its magmatic and degassing systems in relation to new developments in DLF-earthquakes and magmatic recharging processes and to identify seasonal variation in gas flux, we recommend continual monitoring of geogenic gases in all available springs taken at short temporal intervals.
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
video data from inside a Ruhrstahl Heraeus degasser
<p>An Ruhrstahl Heraeus plant is used in steel making to adjust the chemical composition of the steel right before casting. The process and the setup to generate the images provided in this data set are described more in detail in DOI: 10.1002/srin.202200060. The two major treatment types are degassing and decarbonization. The first focuses on removing desolved gases from the melt. The second includes the removal of carbon from the melt in addition to the degassing.</p> <p>The data set provided here contains images taken from 90 treatments. Each folder contains two subfolders "empty" and "treatment". In the folder "empty" 119 images from the empty chamber right before the treatment starts. The folder "treatment" contains images from the treatment. These have already been filtered using processdata for good viewing conditions. The process data itself is not part of the data set.</p> <p>The INEVITABLE project, carried out in a collaboration with voestalpine Stahl GmbH, has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 869815. This paper reflects only the authors' views, and the European Commission is not responsible for any use that may be made of the information it contains. The authors moreover gratefully acknowledge the funding support of K1-MET GmbH, whose research program is supported by COMET (Competence Center for Excellent Technologies), the Austrian program for competence centers. COMET is funded by the Austrian ministries BMK and BMDW, the provinces of Upper Austria, Tyrol, and Styria, and the Styrian Business Promotion Agency (SFG).</p>
Dataset for "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing"
<p>Code and datasets required to reproduce figures in main text of Warren & Kite 2023 "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing." https://www.pnas.org/doi/full/10.1073/pnas.2209751120</p> <p>Code and usage instructions are also available at: github.com/aowarren/Venus_O2</p> <p>After downloading, code will need to be modified to find files in chosen directories for running the full model (also requires installation of VolcGases from github.com/Nicholaswogan/VolcGases) and files for re-creating plots. All .zip files contain data used to create figures in paper. Instructions to reproduce figures below:</p> <p> </p> <p><strong>1. To reproduce full dataset</strong>, download all files excluding .zip files and install VolcGases. Modify "modular_functions_clean_redox.py" to match VolcGases installation.</p> <p><em>For runaway greenhouse runs:</em> Ensure "modular_functions_clean_redox.py" line 512 is commented out. Run "adding_dissolution_clean.py" followed by "ext1line.py" to pre-run runaway greenhouse model and save output to read into full model (this speeds up running the entire suite of "melting" models, but is not strictly necessary). Next, use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><em>For runs without runaway greenhouse melting:</em> uncomment "modular_functions_clean_redox.py" line 512. Use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><strong>2. To reproduce Figures 2, 3, S1, and S2</strong>, either download all zip files beginning with "Fig2_" and "Fig3_", extract all files to single location, or save all new model output into a single directory and use "plot_together.py" to generate figures (instructions contained within script). Use same script to reproduce Figures S9 and S10 with data in "t_atm_sensitivity.zip" and "bH_sensitivity.zip". </p> <p><strong>3. To reproduce Figures 4 and 5</strong>, If using new model runs to generate plots, first run "gen_data_forplots.py". Alternatively, download:</p> <ul> <li>e_statistics_nomelt_CO_FMQ0.npz </li> <li>e_wd_statistics_melt_CO_FMQ0.npz</li> <li>40_Ar_hab_dlith_KU_mix_52522.csv</li> <li>40_Ar_hab_dlith_KU_nomix_52522.csv</li> </ul> <p>Then, run maintext_plotting_new.py.</p> <p><strong>4. To reproduce Figures S5 to S8</strong>, run "Ar_plots.py". Requires: "serpent_dehyd2.csv" and "eclogite_transition.csv".</p>
Volatile (C, N, Ar) variability in MORB and the respective roles of mantle source heterogeneity and degassing: the case of the Southwest Indian Ridge
<p>Location, isotopic compositions of δ13C and δ18O of CO2, δ15N of N2 and C, N and Ar abundances in vesicles of SWIR basaltic glasses</p>
Dataset for the NC article: Deep mantle earthquakes linked to CO2 degassing at the Mid-Atlantic Ridge
<p>The obtained earthquake catalogue, picked P- and S-arrivals, and 1-D velocity models in the Mid-Atlantic Ridge in the equatorial Atlantic ocean, using a recent temporary array of seafloor seismometers.</p> <p>Related article:<br>Yu, Z., Singh, S.C., Hamelin, C. <em>et al.</em> Deep mantle earthquakes linked to CO<sub>2</sub> degassing at the mid-Atlantic ridge. <em>Nat Commun</em> <strong>16</strong>, 563 (2025). https://doi.org/10.1038/s41467-024-55792-9</p>
Impact Degassing of H2 on Early Mars and Its Effect on the Climate System
<p>Supporting information contains IDL saveset files to reproduce figures, and IDL source code to run the model, and an excel spreadsheet of crater statistics. </p>
Data from: Constraining soil hydrothermal CO2 degassing across the Changbaishan volcanic area: insights from 13C-14C perspective
Open the record for dataset details and reuse information.
Output files for Degassing of CO2 triggers large-scale loss of helium from magma oceans
<p>This upload includes output files for the paper Degassing of CO2 triggers large-scale loss of helium from magma oceans.</p>
mzhangrocks/KarakoramFault: Data files for hydrothermal degassing from the Karakoram fault
<p>Tables S1–S2 and Data Sets S1–S2 for geochemical study on hydrothermal degassing from the Karakoram fault in western Tibetan Plateau</p>
Elemental and S isotopic composition data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source"
<p>Data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source".</p>
A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge. Part A: Degassing processes
<p>All data appearing in the manuscript and any supplementary documents, figures, or tables of the paper "A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge.<strong> </strong>Part A: Degassing processes"<strong> </strong>at Geochimica et Cosmochimica Acta<strong> </strong>by<strong> </strong>Bekaert et al., are available through this open access data repository.</p>
Output files for Degassing of Noble Gases from the Magma Ocean
<p>This upload includes output files for the paper Degassing of noble gases from the magma ocean.</p>
Desorption (degassing) of CO2 out of subbituminous coal
<p>The desorption process is observed as CO2 degassing out of the coal. After flooding the coal with CO2 up to 1600 psi, the sub-bituminous coal sample is removed from the pressure chamber and exposed to atmospheric conditions. The CO2 desorption occurs mostly through the cleats and fractures in the coal (highlighted with white marker), evidenced by the stream of bubbles of the soapy water. Also, tiny bubbles burst randomly in the coal bulk matrix, indicating the location of microfractures conducting the CO2.</p> <p>Details of the research in: Vega-Ortiz, Carlos . 2021. Optimization of CO2 Mass Transport and Storage at In-situ Conditions in Two Unconventional Plays: Coalbed Methane and Carbonaceous Mudstones. Ph.D. Thesis. The University of Utah.</p>
Biocatalytic Nanoparticles for the Stabilization of Degassed Single Electron Transfer Living Radical Pickering Emulsion Polymerizations
<p>Raw data for the figures in the main article in Nat. Commun.</p>
Changes in diffuse degassing from the summit crater of Teide volcano (Tenerife, Canary Islands) prior to the 2016 Tenerife long-period seismic swarm
<p>Data paper "Changes in diffuse degassing from the summit crater of Teide volcano (Tenerife, Canary Islands) prior to the 2016 Tenerife long-period seismic swarm".</p>
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