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Partial melting of crustal and mantle rocks under pressure from impedance spectroscopy measurements
(2004)
The purpose of this work is to achieve a better understanding of the physical properties of rocks during partial melting processes. The electrical conductivity of some crustal and upper mantle rocks was measured prior and above the melting under pressure. The variations of the electrical conductivity were compared with the distribution of melt in partially molten rock samples. The electrical conductivity was estimated from the impedance spectroscopy at temperatures between 800 and 1450˚C and at pressures between 0.3 and 2 GPa. These measurements were performed in a piston cylinder apparatus. At temperatures above the melting, samples were equilibrated during a long time and subsequently quenched. Thin sections were prepared and topology, volume fraction and chemical composition of melt was analyzed by using a microprobe. Above the solidus temperature, the electrical conductivity increases for about 1 to 2 orders of magnitude in comparison with non-melted rocks. The "melt effect" seems to reflect the formation of an interconnected network of melt. When a complete melt connectivity is established, the charge transport follows the network of the formed melt films at grain boundaries. Usually, it takes a long time in order to reach a steady state of the electrical resistance in partially molten rocks. Only when a steady state of the electrical resistance is achieved, the bulk conductivity of a sample can be measured properly. The time-independent electrical conductivity were found only after 200 h of annealing time at a desired temperature.
Usually, the measurements of a dihedral angle on grain-liquid interfaces in rocks show that the wetting of grain faces start to develop at temperatures slightly above the solidus temperature. The development of these faces should lead to a continuous melt network even at small melt fractions of few wt.%. This result is not confirmed by our electrical conductivity measurements. The complete interconnection of the melt phase, which was mark by an increase of the electrical conductivity, corresponds to a temperature significantly above the solidus temperature, for at least 30-50˚C. The development of these faces stimulate a significant increase of the electrical conductivity, and corresponds to the occurence of at least 5 wt.% of a melt fraction. This result could be explained by deviations from the textural equilibrium of a melt phase topology in partially molten samples due to heterogeneous grain size distribution, misorientation of grains and anisotropy of the superficial energy of adjacent grain boundaries.
Some mixing models that allow to calculate the electrical conductivity of a composite as a function of a melt fraction were examined and the results of these calculations are discussed.
The experimental results were compared to the conductivity data obtained from magnetotelluric and electromagnetic measurements in the Northern part of mid-Atlantic ridge where a series of magma chambers are presumably located. There is a good agreement between our conductivity values for a melt fraction of 10-13 the conductivity estimated in the Reykjanes ridge zone.
Highlights
• We find DBrfluid/melt = 1.19 to 3.92 for experimental Br degassing from basalt magma into aqueous fluids.
• D <1 under almost dry conditions propose only little Br degassing for dry intra-plate volcanism relative to volcanic arcs.
• An annual global Br flux of 23.5–72.9 × 109 g/y into the atmosphere was calculated.
Abstract
We present the first in-situ partitioning data for bromine between a natural basaltic melt and a coexisting fluid. For this study hydrothermal diamond anvil cell experiments at pressures up to 1.7 GPa were conducted. We combined laser heating to melt the basalt glass with external heating to lower the temperature gradient in the cell and to initiate circulation for the aqueous fluid. Bromine concentrations were measured in-situ with X-ray fluorescence in the basaltic melts, glasses, and in the fluid. From the results we calculated partition coefficients of DBrfluid/melt = 1.19 to 3.92 in the range of 0.4 to 1 GPa for aqueous fluids. Experiments with neon as the surrounding fluid (DBrfluid/melt = 0.38 ± 0.01 at 1.1 GPa) suggest that Br-release from a basalt into volatiles that have no bonding affinity with Br is weak. This should be the case for dry intra-plate volcanic eruptions. From the experimentally gained partition coefficients and from global Br concentration values in melt inclusions of arc magmas, we calculated an annual global Br flux of 23.5–72.9 × 109 g/y.
We present a new experimental dataset on the impact of the heavy halogens chlorine, bromine and iodine on the Raman water bands concerning pressure and their concentration at room temperature. These experiments were conducted at ambient temperature, with variations in halogen concentration and pressure ranging from 0 to 1.4 GPa.
The strength of the Raman water band shift change increases with the ionic size from chlorine, over bromine, to iodine. Our experiments further demonstrate that increased pressure diminishes the impact of the halogen shift change to a varying extent for each of the three halogens. This finding can have significant implications for the salinity calculation of fluid inclusions in minerals such as quartz or olivine. Particularly in the low salinity range, the concentration can be markedly underestimated if the pressure effect is neglected. For experiments in diamond anvil cells involving halogens dissolved in water, the change in Raman water band shifts can serve either as a new tool to monitor pressure, or to monitor the salinity.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
(2022)
Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
Highlights
• Germany plans more long-distances water transfers to secure drinking water supply.
• Long-distance water transfers can unfold lock-ins that limit adaptive water governance.
• Our interdisciplinary case study shows how lock-ins emerge over different spaces and times.
• Commercialisation of water but also local protests contributed to various lock-ins.
• We therefore call for context-specific assessments of potentials and risks of LDWT.
Abstract
Germany plans to expand water transfers over long distances in the light of numerous and pressing challenges for drinking water supply. Research on inter- and intrabasin water transfers warns, however, that major investments in large-scale infrastructure systems accompanied by institutional logics and political interests often lead to a so-called lock-in. As a consequence, long-distance water transfers can limit the potential for adaptive water governance in the involved supply areas over decades with negative impacts for people and the environment. By using a case study in Germany as an example, we researched when, where and how such lock-ins around long-distance water transfers emerge. In the infrastructural development of the Elbaue-Ostharz transfer system we found various lock-ins that overlap in space and time. Some are located at the centre others at the margins of the infrastructure and commercialization of the water sector as well as hydraulic and hygienic concerns interlock with local protests in a way that the expansion of the long-distance water transfer infrastructure is presented continuously as imperative. Our findings contribute to a relational understanding of lock-ins of long-distance water transfers as contingent and diverse processes. Given the widespread occurrence of lock-ins, we argue for a context-specific assessment of potentials and risks of long-distance water transfers in times of multiple crises.
Highlights
• New fumarole and thermal water data for Askja and Kverkfjöll volcanoes, Iceland.
• Data compared to modelled compositions and fluxes of magmatic gas.
• Fumarole compositions compatible with origin of CO2 and S from degassing intrusions.
• Intrusive magmatic fluxes sufficient to sustain hydrothermal fluxes of CO2 and S in Iceland
• Magma degassing insignificant/minor source of H2O and Cl to Icelandic hydrothermal fluids
Abstract
Mantle volatiles are transported to Earth's crust and surface by basaltic volcanism. During subaerial eruptions, vast amounts of carbon, sulfur and halogens can be released to the atmosphere during a short time-interval, with impacts ranging in scale from the local environment to the global climate. By contrast, passive volatile release at the surface originating from magmatic intrusions is characterized by much lower flux, yet may outsize eruptive volatile quantities over long timescales. Volcanic hydrothermal systems (VHSs) act as conduits for such volatile release from degassing intrusions and can be used to gauge the contribution of intrusive magmatism to global volatile cycles. Here, we present new compositional and isotopic (δD and δ18O-H2O, 3He/4He, δ13C-CO2, Δ33S-δ34S-H2S and SO4) data for thermal waters and fumarole gases from the Askja and Kverkfjöll volcanoes in central Iceland. We use the data together with magma degassing modelling and mass balance calculations to constrain the sources of volatiles in VHSs and to assess the role of intrusive magmatism to the volcanic volatile emission budgets in Iceland.
The CO2/ΣS (10−30), 3He/4He (8.3–10.5 RA; 3He/4He relative to air), δ13C-CO2 (−4.1 to −0.2 ‰) and Δ33S-δ34S-H2S (−0.031 to 0.003 ‰ and −1.5 to +3.6‰) values in high-gas flux fumaroles (CO2 > 10 mmol/mol) are consistent with an intrusive magmatic origin for CO2 and S at Askja and Kverkfjöll. We demonstrate that deep (0.5–5 kbar, equivalent to ∼2–18 km crustal depth) decompression degassing of basaltic intrusions in Iceland results in CO2 and S fluxes of 330–5060 and 6–210 kt/yr, respectively, which is sufficient to account for the estimated CO2 flux of Icelandic VHSs (3365–6730 kt/yr), but not the VHS S flux (220–440 kt/yr). Secondary, crystallization-driven degassing from maturing intrusions and leaching of crustal rocks are suggested as additional sources of S. Only a minor proportion of the mantle flux of Cl is channeled via VHSs whereas the H2O flux remains poorly constrained, because magmatic signals in Icelandic VHSs are masked by a dominant shallow groundwater component of meteoric water origin. These results suggest that the bulk of the mantle CO2 and S flux to the atmosphere in Iceland is supplied by intrusive, not eruptive magmatism, and is largely vented via hydrothermal fields.
Highlights
• Subcrustal earthquakes detected beneath Fogo volcano, Cape Verde.
• At the focal depth of 40 km temperatures are likely too high for brittle failure.
• The earthquakes may originate from magma injection into a deep subcrustal reservoir.
• This observation indicates a distinct magma supply system of Fogo volcano.
Abstract
Fogo volcano belongs to the Cape Verde hotspot and its most recent eruption occurred from November 2014 to February 2015. From January to December 2016 we operated a temporary seismic network and array on Fogo and were able to locate 289 earthquakes in total. Array analysis shows that most of the events occur within the crust at distances >25 km near the neighboring island of Brava. However, on 15th August 2016 the network recorded an isolated cluster of >20 earthquakes, 13 of which could be located beneath the southern part of Fogo. The differences between S- and P-wave arrival times at steep incidence clearly indicate focal depths between approximately 38 and 44 km whereas receiver-function analyses place the Moho discontinuity at depths between 11 and 14 km. Thus, the earthquakes are located well within the upper mantle directly beneath Fogo. In view of the elevated upper-mantle temperatures within a hotspot regime, we propose that fracturing induced by magmatic injection is the most likely cause for the observed deep earthquakes.
Highlights
• Full automatized analysis of teleseismic XKS shear wave splitting.
• Rapid analysis of large seismological data sets.
• Automated window selection and quality classification.
• Application to the USArray Transportable Array including expansion to Alaska.
• Improved statistical evidence and objectivity of derived effective splitting.
Abstract
Recent technological advances have led to community wide use of large-scale seismic experiments which produce seismic data on previously impossible scales. Standard processing procedures thus require automatization to facilitate a fast and objective analysis of the data. Among these, XKS-splitting is an important tool to derive first insights into the Earth's deformation regimes at depth by studying seismic anisotropy. Most often, shear-wave splitting is interpreted to represent crystallographic preferred orientation (CPO) of mantle minerals like olivine as dominating feature and can thus be used as a proxy of mantle flow processes. Here, we introduce an addition to the MATLAB®-based SplitRacer tool box (Reiss and Rümpker 2017) which automatizes the entire XKS-splitting procedure. This is achieved by the automatization of 1) choosing a time window based on spectral analyses and 2) categorization of results based on three different XKS-splitting methods (energy minimization, rotation correlation and splitting intensity). This provides effective and objective results for splitting as well as null-measurement results. This extension allows to use SplitRacer without a graphical interface and introduces a bootstrapping statistics as error estimate of the single layer joint splitting method. The procedures are designed to allow a fast and more objective analysis of a vast amount of data, as produced by recent seismic deployments (e.g. USArray, AlpArray). We test this automatization by applying the analysis to the USArray data set, which has approximately 1900 stations with between two to fifteen years of data. We can reproduce the general pattern of the results from former studies with the more objective automatic analysis. Based on a joint-splitting approach, we approximate the splitting effect at individual stations by a single anisotropic layer. As we include null-measurements as well as a larger data set as previous studies, we can provide improved statistical evidence for these effective splitting parameters.
Highlights
• We show the first observations of seismo-acoustic tremor at Oldoinyo Lengai, the world's only active carbonatite volcano.
• We observe significant changes in seismic and acoustic tremor properties and their correlation in one year of data collection.
• Using satellite-based thermal data, we identify different volcanic processes (degassing, lava pond dynamics and spattering).
Abstract
We analyze volcanic tremor from Oldoinyo Lengai, Tanzania, which is currently the only active volcano on Earth producing carbonatitic lavas. Here, we use data from the recent SEISVOL deployment and focus on a co-located seismic and infrasound station about 200 m below the summit. We show the very first observations of seismo-acoustic tremor caused by carbonatitic eruptions. This seismo-acoustic tremor is highly variable throughout the ∼one year of data which we characterize by analyzing its seismic amplitude, duration, recurrence, dominant seismic frequency and harmonics. Frequency gliding occurs frequently and over short (minutes to hours) to long time scales (hours to days) and likely reflects different time-dependent mechanisms, such as evenly-spaced repeating events with a change in inter-event times, crater dynamics that alter resonators, and dike intrusions. Seismic and acoustic wavefields correlate well for stronger eruptive sequences but are only partially coherent which suggests that high-frequency seismic tremor (up to 25 Hz) may be caused by the low viscosity of the carbonatitic melt and not by ground-coupled airwaves. In addition, the comparison between seismic-acoustic and satellite InfraRed thermal data allows us to infer different volcanic activity styles which partially alternate throughout the year: intrusive activity and the construction of hornitos, degassing, activity from a lava pond, and varying styles of extrusive activity, in particular spattering. Our study provides important insights into the eruption dynamics of this peculiar volcano which suggests shallow melt storage within the crater floor.