Tatsu Kuwatani

Ongoing hydrothermal activities within Enceladus

Detection of sodium-salt-rich ice grains emitted from the plume of the Saturnian moon Enceladus suggests that the grains formed as frozen droplets from a liquid water reservoir that is, or has been, in contact with rock. Gravitational field measurements suggest a regional south polar subsurface ocean of about 10xa0kilometres thickness located beneath an ice crust 30 to 40 kilometres thick. These findings imply rock–water interactions in regions surrounding the core of Enceladus. The resulting chemical ‘footprints’ are expected to be preserved in the liquid and subsequently transported upwards to the near-surface plume sources, where they eventually would be ejected and could be measured by a spacecraft. Here we report an analysis of silicon-rich, nanometre-sized dust particles (so-called stream particles) that stand out from the water-ice-dominated objects characteristic of Saturn. We interpret these grains as nanometre-sized SiO2 (silica) particles, initially embedded in icy grains emitted from Enceladus’ subsurface waters and released by sputter erosion in Saturn’s E ring. The composition and the limited size range (2 to 8 nanometres in radius) of stream particles indicate ongoing high-temperature (>90xa0°C) hydrothermal reactions associated with global-scale geothermal activity that quickly transports hydrothermal products from the ocean floor at a depth of at least 40xa0kilometres up to the plume of Enceladus.

Precise estimation of pressure–temperature paths from zoned minerals using Markov random field modeling: theory and synthetic inversion

The chemical zoning profile in metamorphic minerals is often used to deduce the pressure–temperature (P–T) history of rock. However, it remains difficult to restore detailed paths from zoned minerals because thermobarometric evaluation of metamorphic conditions involves several uncertainties, including measurement errors and geological noise. We propose a new stochastic framework for estimating precise P–T paths from a chemical zoning structure using the Markov random field (MRF) model, which is a type of Bayesian stochastic method that is often applied to image analysis. The continuity of pressure and temperature during mineral growth is incorporated by Gaussian Markov chains as prior probabilities in order to apply the MRF model to the P–T path inversion. The most probable P–T path can be obtained by maximizing the posterior probability of the sequential set of P and T given the observed compositions of zoned minerals. Synthetic P–T inversion tests were conducted in order to investigate the effectiveness and validity of the proposed model from zoned Mg–Fe–Ca garnet in the divariant KNCFMASH system. In the present study, the steepest descent method was implemented in order to maximize the posterior probability using the Markov chain Monte Carlo algorithm. The proposed method successfully reproduced the detailed shape of the synthetic P–T path by eliminating appropriately the statistical compositional noises without operator’s subjectivity and prior knowledge. It was also used to simultaneously evaluate the uncertainty of pressure, temperature, and mineral compositions for all measurement points. The MRF method may have potential to deal with several geological uncertainties, which cause cumbersome systematic errors, by its Bayesian approach and flexible formalism, so that it comprises potentially powerful tools for various inverse problems in petrology.

Development of a predictive model for lead, cadmium and fluorine soil–water partition coefficients using sparse multiple linear regression analysis

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models.

Statistical model selection between elastic and Newtonian viscous matrix models for the microboudin palaeopiezometer

We carried out statistical evaluations of two probability density functions for the microboudin palaeopiezometer using the Akaike information criterion (AIC) and the cross-validation (CV) technique. In terms of the relevant stress-transfer model, these functions are defined as the elastic matrix model and the Newtonian viscous matrix model, respectively. The AIC and CV techniques enable us to evaluate the relative quality of both models when applied to nine data sets collected from metachert samples containing tourmaline grains in a quartz matrix, collected from the East Pilbara Terrane, Western Australia. The results show that the elastic matrix model is the more appropriate probability density function for analysis of fracturing of tourmaline grains in a quartz matrix. This statistical evaluation shows the validity of the elastic matrix model for the microboudin palaeopiezometer when analysing such data sets.Graphical abstract.

Progressive evolution of whole–rock composition during metamorphism revealed by multivariate statistical analyses

The geochemical evolution of metamorphic rocks during subduction-related metamorphism is described on the basis of multivariate statistical analyses. The studied dataset comprises a series of mapped metamorphic rocks collected from the Sanbagawa metamorphic belt in central Shikoku, Japan, where metamorphic conditions range from the pumpellyite–actinolite to epidote–amphibolite facies. Recent progress in computational and information science provides a number of algorithms capable of revealing structures in large datasets. This study applies k-means cluster analysis (KCA) and non-negative matrix factorization (NMF) to a series of metapelites, which is the main lithotype of the Sanbagawa metamorphic belt. KCA describes the structures of the high-dimensional data, while NMF provides endmember decomposition which can be useful for evaluating the spatial distribution of continuous compositional trends. The analyzed dataset, derived from previously published work, contains 296 samples for which 14 elements (Si, Ti, Al, Fe, Mn, Mg, Ca, Na, K, P, Rb, Sr, Zr and Ba) have been analyzed. The KCA and NMF analyses indicate five clusters and four endmembers, respectively, successfully explaining compositional variations within the dataset. n nKCA indicates that the chemical compositions of metapelite samples from the western (Besshi) part of the sampled area differ significantly from those in the east (Asemigawa). In the west, clusters show a good correlation with the metamorphic grade. With increasing metamorphic grade, there are decreases in SiO2 and Na2O and increases in other components. On the other hand, the compositional change with metamorphic grade is less obvious in the eastern area. Endmember decomposition using NMF revealed that the evolutional change of whole rock composition, as correlated with metamorphic grade, approximates a stoichiometric increase of a garnet-like component in the whole–rock composition, possibly due to the precipitation of garnet and effusion of other components during progressive dehydration. Thermodynamic modelling of the evolution of the whole–rock composition yielded the following results: (1) the whole-rock composition at lower metamorphic grade favours the preferential crystallization of garnet under the conditions of the garnet zone, with biotite becoming stable together with garnet in higher-grade rock compositions under the same P-T conditions; (2) with higher-grade whole–rock compositions, more H2O is retained. These results provide insight into the mechanism suppressing dehydration under high-pressure metamorphic conditions. This mechanism should be considered in forward modelling of the fluid cycle in subduction zones, although such a quantitative model has yet to be developed. n nThis article is protected by copyright. All rights reserved.

Discontinuous boundaries of slow slip events beneath the Bungo Channel, southwest Japan

The down-dip limit of the seismogenic zone and up-dip and down-dip limits of the deep low-frequency tremors in southwest Japan are clearly imaged by the hypocentre distribution. Previous studies using smooth constraints in inversion analyses estimated that long-term slow slip events (L-SSEs) beneath the Bungo Channel are distributed smoothly from the down-dip part of the seismogenic zone to the up-dip part of the tremors. Here, we use fused regularisation, a type of sparse modelling suitable for detecting discontinuous changes in the model parameters to estimate the slip distribution of L-SSEs. The largest slip abruptly becomes zero at the down-dip limit of the seismogenic zone, is immediately reduced to half at the up-dip limit of the tremors, and becomes zero near its down-dip limit. Such correspondences imply that some thresholds exist in the generation processes for both tremors and SSEs. Hence, geodetic data inversion with sparse modelling can detect such high resolution in the slip distribution.

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.