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Upper mantle shear zones are complex systems where deformation is commonly closely interacting with metamorphic (solid-solid) and/or melt/fluid-rock reactions. Here, feedback processes between deformation, reactions, grain size reduction and phase mixing result in strain weakening and the localization of deformation. The expression of these interlinked processes is portrayed by the microfabrics of strained peridotites and pyroxenites. The present thesis is focusing on these processes and their impact on the deformation in three upper mantle shear zones situated in the peridotite massifs of Lanzo (Italian Alps), Erro-Tobbio (Italian Alps) and Ronda (Betic Cordillera, Spain). In all three shear zones, the presence of melt led to phase mixing either by interstitial crystallization of pyroxenes from a Si-saturated and partially also highly evolved melt or by melt-rock reactions of pyroxene porphyroclasts with a Si-undersaturated melt. The effect of melt on the localization of strain is twofold and variable. Enhanced deformation by melt-wetted boundaries is assumed for all shear zones. Additionally, phase mixing by crystallization of interstitial pyroxenes or melt-rock reactions reduce or maintain the grain size by the formation of fine grained neoblasts and secondary phase boundary pinning. In this regard, pre- to early syn-kinematic, map-scale percolation of OH-bearing, evolved melts in the NW Ronda peridotite massif and the associated crystallization of interstitial pyroxenes result in the activation of grain size sensitive deformation mechanisms in the entire melt-effected area. In the rocks collected at Erro-Tobbio, syn-kinematic melt-rock reactions of pyroxene porphyroclasts and Si-undersaturated melt led to the formation of ultramylonitic neoblast tails (grain size ~10 μm). Compared to the adjacent coarser-grained olivine-dominated matrix, the activation of diffusion creep led to an increase in the strain rate by an order of magnitude within interconnected ultramylonitic layers. Strain localization and softening in ultramylonitic layers are also documented in the Lanzo samples. Neoblast tails of pyroxene porphyroclasts were likewise identified as their precursor. The phase assemblage of the tails, including ortho- and clinopyroxene, olivine, plagioclase, and spinel (± amphibole), and their geochemical trends suggest, unlike in Erro-Tobbio, a formation by continuous net-transfer reactions enhanced by the spinel lherzolite to plagioclase lherzolite transition.
The new results obtained from the three studied shear zones underscore the importance of reactions for the interlinked processes of grain size reduction, phase mixing, strain localization and strain softening in upper mantle shear zones. Concerning strain localization, the nature of the reaction (solid-solid, melt/fluid-rock) seems to play a subordinate role compared to its timing. Pre- to early syn-kinematic melt-triggered reactions result in strain localization along map-scale shear zones. Late stage syn-kinematic melt-rock or metamorphic reactions under high stress conditions are capable of localizing the deformation along discrete, sub-centimeter thick ultramylonites.
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.
The metasomatised continental mantle may play a key role in the generation of some ore deposits, in particular mineral systems enriched in platinum-group elements (PGE) and Au. The cratonic lithosphere is the longest-lived potential source for these elements, but the processes that facilitate their pre-concentration in the mantle and their later remobilisation to the crust are not yet well-established. Here, we report new results on the petrography, major-element, and siderophile- and chalcophile-element composition of native Ni, base metal sulphides (BMS), and spinels in a suite of well-characterised, highly metasomatised and weakly serpentinised peridotite xenoliths from the Bultfontein kimberlite in the Kaapvaal Craton, and integrate these data with published analyses. Pentlandite in polymict breccias (failed kimberlite intrusions at mantle depth) has lower trace-element contents (e.g., median total PGE 0.72 ppm) than pentlandite in phlogopite peridotites and Mica-Amphibole-Rutile-Ilmenite-Diopside (MARID) rocks (median 1.6 ppm). Spinel is an insignificant host for all elements except Zn, and BMS and native Ni account for typically <25% of the bulk-rock PGE and Au. High bulk-rock Te/S suggest a role for PGE-bearing tellurides, which, along with other compounds of metasomatic origin, may host the missing As, Ag, Cd, Sb, Te and, in part, Bi that are unaccounted for by the main assemblage.
The close spatial relationship between BMS and metasomatic minerals (e.g., phlogopite, ilmenite) indicates that the lithospheric mantle beneath Bultfontein was resulphidised by metasomatism after initial melt depletion during stabilisation of the cratonic lithosphere. Newly-formed BMS are markedly PGE-poor, as total PGE contents are <4.2 ppm in pentlandite from seven samples, compared to >26 ppm in BMS in other peridotite xenoliths from the Kaapvaal craton. This represents a strong dilution of the original PGE abundances at the mineral scale, perhaps starting from precursor PGE alloy and small volumes of residual BMS. The latter may have been the precursor to native Ni, which occurs in an unusual Ni-enriched zone in a harzburgite and displays strongly variable, but overall high PGE abundances (up to 81 ppm). In strongly metasomatised peridotites, Au is enriched relative to Pd, and was probably added along with S. A combination of net introduction of S, Au +/− PGE from the asthenosphere and intra-lithospheric redistribution, in part sourced from subducted materials, during metasomatic events may have led to sulphide precipitation at ~80–120 km beneath Bultfontein. This process locally enhanced the metallogenic fertility of this lithospheric reservoir. Further mobilisation of the metal budget stored in these S-rich domains and upwards transport into the crust may require interaction with sulphide-undersaturated melts that can dissolve sulphides along with the metals they store.
We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.