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  • B and Sr isotopes  (1)
  • Cross-correlations  (1)
  • Forward gravity modeling  (1)
  • GPS time series  (1)
  • 1
    Publication Date: 2023-06-23
    Description: The Izu–Bonin–Mariana volcanic arc is situated at a convergent plate margin where subduction initiation triggered the formation of MORB-like forearc basalts as a result of decompression melting and near-trench spreading. International Ocean Discovery Program (IODP) Expedition 352 recovered samples within the forearc basalt stratigraphy that contained unusual macroscopic globular textures hosted in andesitic glass (Unit 6, Hole 1440B). It is unclear how these andesites, which are unique in a stratigraphic sequence dominated by forearc basalts, and the globular textures therein may have formed. Here, we present detailed textural evidence, major and trace element analysis, as well as B and Sr isotope compositions, to inves tigate the genesis of these globular andesites. Samples consist of K2 O-rich basaltic globules set in a glassy groundmass of andesitic composition. Between these two textural domains a likely hydrated interface of devitrified glass occurs, which, based on textural evidence, seems to be genetically linked to the formation of the globules. The andesitic groundmass is Cl rich (ca. 3000 𝜇g/g), whereas globules and the interface are Cl poor (ca. 300 𝜇g/g). Concentrations of fluid-mobile trace elements also appear to be fractionated in that globules and show enrichments in B, K, Rb, Cs, and Tl, but not in Ba and W relative to the andesitic groundmass, whereas the interface shows depletions in the latter, but is enriched in the former. Interestingly, globules and andesitic groundmass have identical Sr isotopic composition within analytical uncertainty (87 Sr∕86 Sr of 0.70580 ± 10), indicating that they likely formed from the same source. However, globules show high 𝛿11B (ca. + 7‰), whereas their host andesites are isotopically lighter (ca. – 1 ‰), potentially indicating that whatever process led to their formation either introduced heavier B isotopes to the globules, or induced stable isotope fractionation of B between globules and their groundmass. Based on the bulk of the textural information and geochemical data obtained from these samples, we conclude that these andesites likely formed as a result of the assimilation of shallowly altered oceanic crust (AOC) during forearc basaltic magmatism. Assimilation likely introduced radiogenic Sr, as well as heavier B isotopes to comparatively unradiogenic and low 𝛿11 B forearc basalt parental magmas (average 87 Sr∕86 Sr of 0.703284). Moreover, the globular textures are consistent with their formation being the result of fluid-melt immiscibility that was potentially induced by the rapid release of water from assimilated AOC whose escape likely formed the interface. If the globular textures present in these samples are indeed the result of fluid-melt immiscibility, then this process led to significant trace element and stable isotope fractionation. The textures and chemical compositions of the globules highlight the need for future experimental studies aimed at investigating the exsolution process with respect to potential trace element and isotopic fractionation in arc magmas that have perhaps not been previously considered.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Ruhr-Universität Bochum (1007)
    Keywords: ddc:552.4 ; Izu-Bonin forearc ; Andesites ; Globular textures ; Liquid immiscibility ; B and Sr isotopes
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2023-07-03
    Description: The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN).
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:526 ; Regression time series ; Vector-autoregressive model ; Cross-correlations ; Multivariate scaled t-distribution ; Self-tuning robust estimator ; Generalized expectation maximization algorithm ; Iteratively reweighted least squares ; Multivariate portmanteau test ; Monte Carlo simulation ; GPS time series
    Language: English
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  • 3
    Publication Date: 2023-06-09
    Description: We present an accurate method for the calculation of gravitational potential (GP), vector (GV), and gradient tensor (GGT) of a tesseroid, considering a density model in the form of a polynomial up to cubic order along the vertical direction. The method solves volume integral equations for the gravitational effects due to a tesseroid by the Gauss–Legendre quadrature rule. A two-dimensional adaptive subdivision technique, which automatically divides the tesseroids near the computation point into smaller elements, is applied to improve the computational accuracy. For those tesseroids having small vertical dimensions, an extension technique is additionally utilized to ensure acceptable accuracy, in particular for the evaluation of GV and GGT. Numerical experiments based on spherical shell models, for which analytical solutions exist, are implemented to test the accuracy of the method. The results demonstrate that the new method is capable of computing the gravitational effects of the tesseroids with various horizontal and vertical dimensions as well as density models, while the evaluation point can be on the surface of, near the surface of, outside the tesseroid, or even inside it (only suited for GP and GV). Thus, the method is attractive for many geodetic and geophysical applications on regional and global scales, including the computation of atmospheric effects for terrestrial and satellite usage. Finally, we apply this method for computing the topographic effects in the Himalaya region based on a given digital terrain model and the global atmospheric effects on the Earth’s surface by using three polynomial density models which are derived from the US Standard Atmosphere 1976.
    Keywords: ddc:550.2 ; Forward gravity modeling ; Gauss–Legendre quadrature ; Tesseroids ; Polynomial density model ; Topographic and atmospheric effects
    Language: English
    Type: doc-type:article
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