Kenta Ueki

Thermodynamic calculations of the polybaric melting phase relations of spinel lherzolite

This study presents a new thermodynamic model for the calculation of phase relations during the melting of anhydrous spinel lherzolite at pressures of 1–2.5 GPa. The model is based on the total energy minimization algorithm for calculating phase equilibria within multicomponent systems and the thermodynamic configuration of Ueki and Iwamori [2013]. The model is based on a SiO2−Al2O3−FeO−Fe3O4−MgO–CaO system that includes silicate melt, olivine, clinopyroxene, orthopyroxene, and spinel as possible phases. The molar Gibbs free energy of the melt phase is modeled quasi-empirically, and the thermodynamic parameters for silicate melt end-member components are calibrated with a polybaric calibration database. The temperatures and pressures used in this newly compiled calibration data set are 1230–1600°C and 0.9–3 GPa, corresponding to the stability range of spinel lherzolite. The modeling undertaken during this study reproduces the general features of experimentally determined melting phase relations of spinel lherzolite at 1–2.5 GPa, including the solidus temperature, the melt composition, the chemical reaction during melting, and the degree of melting. This new thermodynamic modeling also reproduces phase relations of various bulk compositions from fertile to deplete spinel lherzolite and can be used in the modeling of polybaric mantle melting within various natural settings. Comparing the results derived from this new modeling with those produced using previous models indicates that the new approach outlined here, involving a combination of total energy minimization and the direct calibration of melt thermodynamic parameters at pressure and temperature conditions corresponding to mantle melting with a relatively simple melt thermodynamic equation, can accurately model polybaric melting phase relations.

Density and seismic velocity of hydrous melts under crustal and upper mantle conditions

We present a new model for calculating the density of hydrous silicate melts as a function of P, T, H2O concentration, and melt composition. We optimize VPr,Tr, ∂V/∂T, ∂V/∂P, ∂V2/∂T∂P, and K′ of H2O end-member components in hydrous silicate melts, as well as K′ of anhydrous silicate melts, using previously reported experimental results. The parameter set for H2O end-member component in silicate melt optimized in this study is internally consistent with the parameter values for the properties of anhydrous silicate melt reported by Lange and Carmichael (1987, 1990). The model calculation developed in this study reproduces the experimentally determined densities of various hydrous melts, and can be used to calculate the relationships between pressures, temperatures, and H2O concentrations of various hydrous melts from ultramafic to felsic compositions at pressures of 0–4.29 GPa. Using the new parameter set, we investigate the effects of H2O content on the seismic velocity of hydrous melts, as well as seismic velocities in partially molten regions of subduction zones. The results show that water content in silicate melt plays a key role in determining seismic velocity structure, and therefore must be taken into account when interpreting seismic tomography.

Genesis of ultra-high-Ni olivine in high-Mg andesite lava triggered by seamount subduction

The Kamchatka Peninsula is a prominent and wide volcanic arc located near the northern edge of the Pacific Plate. It has highly active volcanic chains and groups, and characteristic lavas that include adakitic rocks. In the north of the peninsula adjacent to the triple junction, some additional processes such as hot asthenospheric injection around the slab edge and seamount subduction operate, which might enhance local magmatism. In the forearc area of the northeastern part of the peninsula, monogenetic volcanic cones dated at <1 Ma were found. Despite their limited spatiotemporal occurrence, remarkable variations were observed, including primitive basalt and high-Mg andesite containing high-Ni (up to 6300 ppm) olivine. The melting and crystallization conditions of these lavas indicate a locally warm slab, facilitating dehydration beneath the forearc region, and a relatively cold overlying mantle wedge fluxed heterogeneously by slab-derived fluids. It is suggested that the collapse of a subducted seamount triggered the ascent of Si-rich fluids to vein the wedge peridotite and formed a peridotite–pyroxenite source, causing the temporal evolution of local magmatism with wide compositional range.

Classification of geochemical data based on multivariate statistical analyses: Complementary roles of cluster, principal component, and independent component analyses

Identifying the data structure including trends and groups/clusters in geochemical problems is essential to discuss the origin of sources and processes from the observed variability of data. An increasing number and high dimensionality of recent geochemical data require efficient and accurate multivariate statistical analysis methods. In this paper, we show the relationship and complementary roles of k-means cluster analysis (KCA), principal component analysis (PCA), and independent component analysis (ICA) to capture the true data structure. When the data are preprocessed by primary standardization (i.e., with the zero mean and normalized by the standard deviation), KCA and PCA provide essentially the same results, although the former returns the solution in a discretized space. When the data are preprocessed by whitening (i.e., normalized by eigenvalues along the principal components), KCA and ICA may identify a set of independent trends and groups, irrespective of the amplitude (power) of variance. As an example, basalt isotopic compositions have been analyzed with KCA on the whitened data, demonstrating clear rock‒tectonic occurrence‒mantle end-member discrimination. Therefore, the combination of these methods, particularly KCA on whitened data, is useful to capture and discuss the data structure of various geochemical systems, for which an Excel program is provided. This article is protected by copyright. All rights reserved.

Geochemical Discrimination and Characteristics of Magmatic Tectonic Settings: A Machine‐Learning‐Based Approach

Geochemically discriminating between magmatism in different tectonic settings remains a fundamental part of understanding the processes of magma generation within the Earths mantle. Here, we present an approach where machine-learning (ML) methods are used for quantitative tectonic discrimination and feature selection using global geochemical datasets containing data for volcanic rocks generated in eight different tectonic settings. This study uses support vector machine, random forest, and sparse multinomial regression (SMR) approaches. All these ML methods with data for 20 elements and 5 isotopic ratios allowed the successful geochemical discrimination between igneous rocks formed in eight different tectonic settings with a discriminant ratio better than 83% for all settings barring oceanic plateaus and back-arc basins. SMR is a particularly powerful and interpretable ML method because it quantitatively identifies geochemical signatures that characterize the tectonic settings of interest and the characteristics of each sample as a probability of the membership of the sample for each setting. We also present the most representative basalt composition for each tectonic setting. The new data provide reference points for future geochemical discussions. Our results indicate that at least 17 elements and isotopic ratios are required to characterize each tectonic setting, suggesting that geochemical tectonic discrimination cannot be achieved using only a small number of elemental compositions and/or isotopic ratios. The results show that volcanic rocks formed in different tectonic settings have unique geochemical signatures, indicating that both volcanic rock geochemistry and magma generation processes are closely connected to the tectonic setting.