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The interactive web page contains supplementary information for a publication by Hensch et al. 2019: "Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany)". Details on the analysis of three tectonic and nine deep low-frequency earthquakes are given, including parameter results, error estimates, and figures. The analysis has been performed using the Grond software package (Heimann et. al 2018).The open source software for seismic source parameter optimization (Grond, Heimann et al., 2018) implements a bootstrap-based method to retrieve solution sub-spaces, parameter trade-offs and uncertainties of earthquake source parameters. Synthetic and observed P and S phase waveforms are restituted to displacement and filtered between 0.5 and 5 Hz in variable frequency ranges, depending on the signal-to-noise ratio (SNR) and the character of the signals. Station amplification factors and transfer functions have been evaluated before the restitution using an empirical calibration method (see Dahm et al., 2018). From waveforms, different types of body wave attributes were calculated, as amplitude spectra, envelopes, and amplitude spectral ratios.The Green's functions (GF) were calculated with the orthonormal propagator method (QSEIS, Wang, 1999; see https://github.com/pyrocko/fomosto-qseis/), for a 1 km grid spacing in a volume of 150 km source-receiver distances and 1 - 50 km source depths. The sampling rate was 40 Hz and the GF include near field terms. All GF are stored in a Pyrocko GF store (Pyrocko toolbox, see Heimann et al., 2017). We use a nearest neighbor interpolation in between the grid points of the pre-computed GF.Restituted observed and synthetic ground displacement time series are filtered and windowed between [-2 s; +3 s] from the expected phase arrival, given the tested candidate source model at each forward modeling step in the optimization. Additional to full waveforms, amplitude spectra, envelopes and spectral ratios between P-SV and SH-SV waves are compared. For spectral ratios, a water level approach was implemented to avoid bias from high noise. All components of the mixed inversion received a proper linear weighting with factors between 0.5 and 3, which was selected after running tests with some master events. Weighting and frequency range were defined different for earthquakes with magnitudes above or below ML 2. P and S phase arrivals have been picked to ensure correct selection of time windows during the centroid inversion, and station blacklists were considered event-wise, depending on the SNR.The plots show for every event the data fits and different types of solution plots. The naming of pages is self-explanatory, but more information can be found in the Grond documentation (https://pyrocko.org/grond/). In order to evaluate the ensembles of solutions for interpretation, we extended the standard statistical analysis of Grond to consider a cluster analysis of source mechanism distributions before statistical analysis. This is introduced because the best ensemble solutions of many of the DLF events show higher variability and groups of different mechanisms. A simple mean or median does not always represent the families of best performing solutions. We therefore declustered the ensemble of best solutions using the method of Cesca et al. (2013), applying the Kagan angle norm, and performed the statistical analysis for each individual cluster.
These data files contain short periods of electrical data recorded at Stromboli volcano, Italy, in 2019 and 2020 using a prototype version of the Biral Thunderstorm Detector BTD-200. This sensor consists of two antennas, the primary and secondary antenna, which detect slow variations in the electrostatic field resulting from charge neutralisation due to electrical discharges. The sensor recorded at three different locations: BTD1 (38.79551°N, 15.21518°E), BTD2 (38.80738°N, 15.21355°E) and BTD3 (38.79668°N, 15.21622°E). Electrical data of the following explosions is provided (each in a separate data file): - Three Strombolian explosions on 12 June 2019 at 12:46:53, 12:49:27 and 12:56:10 UTC, respectively. - A major explosion on 25 June 2019 at 23:03:08 UTC. - A major explosion on 19 July 2020 at 03:00:42 UTC. - A major explosion on 16 November 2020 at 09:17:45 UTC. - A paroxysmal event at 3 July 2019 at 14:45:43 UTC. Each filename indicates the location of the BTD, the starting date and time of the file in UTC, and a short description of the three data columns inside the file (unixtime, primary, secondary). The first column provides the Unix timestamp of each data point, which is the time in seconds since 01/01/1970. All time is provided in UTC. The second column provides the measured voltage [V] recorded by the primary antenna. The third column provides the measured voltage [V] recorded by the secondary antenna.
This data publication provides data from 42 experiments from 2018 and 2019 in the Fragmentation Lab at the Ludwig-Maximilians University Munich (Germany). The experiments were taken out to analyse the influence of the water content and the initial temperature of the pre-experimental sample on the produced electrification in rapid decompression, shock-tube experiments. All samples used in this study are 90-300 μm loose ash samples from the lower Laacher See unit.To carry out this study, we have built up on previous studies by Cimarelli et al. (2014) and Gaudin & Cimarelli (2019b, dataset to be found in Gaudin & Cimarelli, 2019a). A sample of loose ash gets placed in an autoclave. In our study, we have added water in some experiments. Also, a furnace was often used to heat the sample to up to 320 °C. After both water addition and heating, the autoclave gets pressurized using argon gas. Once a target pressure of 9 MPa is reached, the experiment gets triggered by rupturing metal diaphragms, which rapid decompresses the sample and ejects it into a collector tank. This collector tank is made out of steel and electrically insulated from its surrounding, thus working as a Faraday cage (FC), which is able to detect the net charge within at any point during the experiment. We detect discharges on that net charge up to 10 ms after the ejection of the particles.This dataset contains:- an overview .xlsx file (ExperimentOverview) containing key information for the 42 experiments used for analysis in this study- raw .csv files for all experiments- .pdf files showing the key elements of the analysed experiments, incl. data from Faraday cage and pressure sensorsFor more information please refer to the data description and the associated publication (Stern et al., 2019).
This data publication provides data from 13 experiments performed in 2022 in the Gas-mixing lab at the Ludwig Maximilian University of Munich (Germany). The experiments were conducted to investigate the charging and discharging potential of material collected from a mud volcano from the Salton Sea (GPS-Data 33°12'2.7"N 115°34'41.4"W). The sample material was used in decompression experiments. The material was pressurized with argon gas instead of methane to assure safety conditions while running the experiments in the laboratory. The experimental setup is a modified version first developed by Alidibirov and Dingwell (1996) and further modified by Cimarelli et al. (2014); Gaudin and Cimarelli (2019); Stern et al. (2019) to enable the detection and quantification of discharges caused by the interaction of the discharging particles. The material was ejected from the autoclave into a Faraday cage, that is insulated from the autoclave and discharges going from the jet to the nozzle were recorded by a datalogger. Additionally, the eruption of the decompressed material was recorded by a high-speed camera. In the experiments, the influence of humidity and grain size distribution were tested. The influence of humidity was tested by using the material as wet as collected but also dried and milled and later exposed to varying but controlled humidity conditions. The grain size distribution was tested by mixing the dried and milled mud sample with 10, 50 and 90% of sea sand.
Data supporting the publication Hornby, AJ, Kueppers U, Maurer BM, Poetsch C and Dingwell DB (2020) "Experimental constraints on volcanic ash generation and clast morphometrics in pyroclastic density currents and granular flows". In this study, fine ash is generated from lapilli-sized volcanic pumice and scoria in rotary tumbler experiments. We seek to explore ash production processes and clast attrition in natural PDCs, and gain insight into the controlling parameters for particle production efficiency with PDC transport distance. We vary the starting mass, apparatus size, and material properties and tumble clasts over multiple transport distance steps from 0.2-6 km. The data are provided in ASCII or image formats as one zipped folder (2020-025_Hornby-et-al_data.zip) and organised in the following sub-folder structure (for more information please consult the associated data description and Hornby et al (2020): (1) Experimental methods, apparatus and data collections (2) Ash generation data for tumbling experiments (3) Laser particle size distribution data for ash generated in tumbling experiments (4) Post-experimental lapilli size (generated via 3-axis caliper measurements), mass, bulk density and dense rock equivalent (DRE) porosity results (5) 2D image analysis morphology results (6) 2D image analysis size results (7) Cropped, scaled and thresholded images lapilli used for morphometric analysis (8) Image analysis macros and workflow for ImageJ (9) Integrated analysis of results This project has received funding from the European Union's Horizon 2020 research and innovation programme.
This data publication provides data from 39 experiments performed in 2021 to 2022 in the Gas-mixing lab at the Ludwig Maximilian University of Munich (Germany). The experiments were conducted to investigate the charging and discharging potential of decompressed soda-lime glass beads in varying enveloping gas composition and two different transporting gas species (argon and nitrogen). The experimental setup is a modified version of an apparatus first developed by Alidibirov and Dingwell (1996) and further modified by Cimarelli et al. (2014), Gaudin and Cimarelli (2019), and Stern et al. (2019) to enable the detection and quantification of discharges caused by the interaction of the discharging particles. The latest modifications enable the setup to perform experiments under gas-tight conditions allowing to test different atmospheric composition and pressure and to sample the gas within the particle collector tank. The sample material was ejected from the autoclave into the particle collector tank that is insulated from the autoclave and works as a Faraday cage. Discharges going from the jet to the nozzle were recorded by a datalogger. Additionally, the ejection of the decompressed material was recorded by a high-speed camera. The gas composition in the collector tank was changed from air to CO2 and a mixture of CO2 and CO. The particle collector tank was conditioned in two different modes: purging three times the tank with the desired gas composition or three times of purging and applying a vacuum in between. Analysis of gas samples taken from the collector tank before conducting the experiments revealed that in both cases a complete removal of the air was not achieved, but significantly reduced by the evacuation-purging method. Two gases were used to pressurize the sample within the autoclave: Nitrogen and Argon. The experimental results were compared to previous experiments (Springsklee et al., 2022a; Springsklee et al., 2022b).
This data publication provides data from 96 experiments from 2020 to 2022 in the gas-mixing lab at the Ludwig-Maximilians-Universität München (Germany). The experiments were conducted to investigate the influence of grain size distribution, especially the influence of very fines [<10 µm] on the generation of experimental volcanic lightning (VL). The influence of grain size distribution was tested for three different materials. Experimental discharges during rapid decompression were evaluated by their number and their total magnitude. The three materials used in this study are a tholeiitic basalt (TB), industrial manufactured soda-lime glass beads (GB) and a phonolitic pumice from the lower Laacher See unit (LSB). The samples were sieved into several grain size fractions, and coarse and fines were mixed to test the influence of the added fines on the discharge behaviour. For the tholeiitic basalt, the coarse grain size fraction is 180-250 µm, for the glass beads 150-250 µm and for the phonolitic pumice, two coarse grain size fractions, 180-250 µm and 90-300 µm were tested. The experiments were carried out in a new experimental setup, a modification of the shock tube experiments first described by Alidibirov and Dingwell (1996) and its further modifications (Cimarelli et al., 2014; Gaudin & Cimarelli, 2019; Stern et al., 2019). A mixture of coarse and fine sample material is placed into an autoclave and continuously set under pressure with argon gas up to the desired decompression pressure (⁓10 MPa). Then, rapid decompression is initialized, and the sample material is ejected from the autoclave through a nozzle into a gas-tight particle collector tank. The particle collector tank is insulated from the nozzle and the ground and serves as a Faraday cage (FC). All discharges going from the erupting gas-particle mixture, the jet, to the nozzle will be recorded by a datalogger. All the discharges measured during the first 5 ms of ejection were taken into the evaluation of the discharge behaviour. The raw signals of the experiments were evaluated by a processing code developed by Gaudin and Cimarelli (2019). Additionally, the jet behaviour was recorded by a high-speed camera: the gas-exit angle and the exit angle of the gas-particle mixture were determined. The background of the high-speed video was divided into a black side and a white side. The gas-exit angle and the exit angle gas-particle-mixture were determined as the mean of the deviation angle of a straight trajectory angle of both sides.
BayStress4 is a package of MatLab routine, designed to constrain the state of stress of a volcanic system by means of posterior Probability Density Functions (PDFs) of the stress tensor components. To do so, it employs the model of three-dimensional (3D) dyke pathways developed by Mantiloni et al., 2023 (SAM: Simplified Analytical Model of dyke Pathways in Three Dimensions) to match the known locations of past eruptive vents to the known or assumed volume in the subsurface ("Dyke nucleation zone" or "D") where their parent dykes nucleated from. This is achieved by a) using SAM to backtrack dyke pathways from the vents down through the crust for a given stress model; b) quantifying the intersection between such pathways and D through a misfit function; c) using this procedure to run a Markov Chain Monte Carlo (MCMC) algorithm to sample the stress parameters' space. The posterior information provided by the stress inversions can then be used to produce forward simulations of dyke pathways with SAM and forecast the surface distribution of future eruptive vents across the volcanic system. This repository contains InVent4Cast, a package of MatLab routines designed to constrain the state of stress of a volcanic system by means of posterior Probability Density Functions (PDFs) of the stress tensor components. To do so, it employs the model of three-dimensional (3D) dyke pathways developed by Mantiloni et al., 2023a (SAM: Simplified Analytical Model of dyke Pathways in Three Dimensions) to match the known locations of past eruptive vents to the known or assumed volume in the subsurface ("Dyke nucleation zone" or "D") where their parent dykes nucleated from. This is achieved by a) using SAM to backtrack dyke pathways from the vents down through the crust for a given stress model; b) quantifying the intersection between such pathways and D through a misfit function; c) using this procedure to run a Markov Chain Monte Carlo (MCMC) algorithm to sample the stress parameters' space. The posterior information provided by the stress inversions can then be used to produce forward simulations of dyke pathways with SAM and forecast the surface distribution of future eruptive vents across the volcanic system. The repository also collects data, figures and results of the application of InVent4Cast to some of the synthetic scenarios of dyke pathways in calderas presented by Mantiloni et al., 2023a. These results were detailed and discussed by Mantiloni et al., 2024a, to which the reader is referred for further information. The synthetic scenarios include numerical models of crustal stress state, focusing on gravitational loading/unloading due to topography and tectonic processes as the dominant stress sources. These stress sources are accounted for by a set of stress parameters. Results include posterior probability density functions (PDFs) of such stress parameters after applying the stress inversion to the scenarios, as well as probability maps of eruptive vent opening across the synthetic volcanic areas. Synthetic scenarios, stress inversions and vent forecasts were produced between May 2022 and November 2023.
Here we present data on the spatiotemporal distribution of seismicity and CO2 flux from the highly active Svartsengi–Eldvörp volcanic system (hereafter referred to as Svartsengi) on the Reykjanes Peninsula. Data were collected between July and September 2024. The area is marked by repeated fissure eruptions associated with rapid magma propagation since November 2023. The eruptions are part of an ongoing volcanic sequence with intermittent pauses. The seismic and gas flux datasets support the idea, that multidisciplinary approaches are important for the identification of potential eruption sites and an improved hazard assessment in volcanic areas.
The Geldingadalir 2021 eruption in Iceland started on 19 March and ended on 18 September. It featured nearly 9000 lava fountain episodes of minute to day duration that were all accompanied by seismic tremor. We measured the duration, repose time, tremor amplitude and shape using seismometers from the University of Potsdam. We publish the corresponding catalogs that contain information about these episodes. Periodically, aerial surveys were conducted by the University of Iceland using unoccupied aerial systems (UAS). These surveys lead to digital surface models (DSM), orthomosaics, and 3D models. These products were used to supplement the seismic observations.
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