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 10 kilometres 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 (>90 °C) hydrothermal reactions associated with global-scale geothermal activity that quickly transports hydrothermal products from the ocean floor at a depth of at least 40 kilometres up to the plume of Enceladus.

It has been suggested that Saturn's moon Enceladus possesses a subsurface ocean. The recent discovery of silica nanoparticles derived from Enceladus shows the presence of ongoing hydrothermal reactions in the interior. Here, we report results from detailed laboratory experiments to constrain the reaction conditions. To sustain the formation of silica nanoparticles, the composition of Enceladus' core needs to be similar to that of carbonaceous chondrites. We show that the presence of hydrothermal reactions would be consistent with NH3- and CO2-rich plume compositions. We suggest that high reaction temperatures (>50 °C) are required to form silica nanoparticles whether Enceladus' ocean is chemically open or closed to the icy crust. Such high temperatures imply either that Enceladus formed shortly after the formation of the solar system or that the current activity was triggered by a recent heating event. Under the required conditions, hydrogen production would proceed efficiently, which could provide chemical energy for chemoautotrophic life.

Geochemically discriminating between magmatism in different tectonic settings remains a fundamental part of understanding the processes of magma generation within the Earth's mantle. Here we present an approach where machine learning (ML) methods are used for quantitative tectonic discrimination and feature selection using global geochemical data sets 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 24 elements and five 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.

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 type/ 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.Plain Language Summary This paper presents a new statistical method that effectively captures the structures of various types of multivariate data (not only geochemical data but also any type of data). The method is based on combinations of k-means cluster analysis, principal component analysis, and independent component analysis for preprocessed data. The corresponding Excel program file is provided.

The timing and thermal effects of granitoid intrusions into accreted sedimentary rocks are important for understanding the growth process of continental crust. In this study, the petrology and geochronology of pelitic gneisses in the Tseel area of the Tseel terrane, SW Mongolia, are examined to understand the relationship between igneous activity and metamorphism during crustal evolution in the Central Asian Orogenic Belt (CAOB). Four mineral zones are recognized on the basis of progressive changes in the mineral assemblages in the pelitic gneisses, namely: the garnet, staurolite, sillimanite and cordierite zones. The gneisses with high metamorphic grades (i.e. sillimanite and cordierite zones) occur in the central part of the Tseel area, where granitoids are abundant. To the north and south of these granitoids, the metamorphic grade shows a gradual decrease. The composition of garnet in the pelitic gneisses varies systematically across the mineral zones, from grossular‐rich garnet in the garnet zone to zoned garnet with grossular‐rich cores and pyrope‐rich rims in the staurolite zone, and pyrope‐rich garnet in the sillimanite and cordierite zones. Thermobarometric analyses of individual garnet crystals reveal two main stages of metamorphism: (i) a high‐P and low‐T stage (as recorded by garnet in the garnet zone and garnet cores in the staurolite zone) at 520–580 °C and 4.5–7 kbar in the kyanite stability field and (ii) a low‐P and high‐T stage (garnet rims in the staurolite zone and garnet in the sillimanite and cordierite zones) at 570–680 °C and 3.0–6.0 kbar in the sillimanite stability field. The earlier high‐P metamorphism resulted in the growth of kyanite in quartz veins within the staurolite and sillimanite zones. The U–Pb zircon ages of pelitic gneisses and granitoids reveal that (i) the protolith (igneous) age of the pelitic gneisses is c. 510 Ma; (ii) the low‐P and high‐T metamorphism occurred at 377 ± 30 Ma; and (iii) this metamorphic stage was coeval with granitoid intrusion at 385 ± 7 Ma. The age of the earlier low‐T and high‐P metamorphism is not clearly recorded in the zircon, but probably corresponds to small age peaks at 450–400 Ma. The low‐P and high‐T metamorphism continued for c. 100 Ma, which is longer than the active period of a single granitoid body. These findings indicate that an elevation of geotherm and a transition from high‐P and low‐T to low‐P and high‐T metamorphism occurred, associated with continuous emplacement of several granitoids, during the crustal evolution in the Devonian CAOB.

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Geochemical discrimination has recently been recognised as a potentially useful proxy for identifying tsunami deposits in addition to classical proxies such as sedimentological and micropalaeontological evidence. However, difficulties remain because it is unclear which elements best discriminate between tsunami and non-tsunami deposits. Herein, we propose a mathematical methodology for the geochemical discrimination of tsunami deposits using machine-learning techniques. The proposed method can determine the appropriate combinations of elements and the precise discrimination plane that best discerns tsunami deposits from non-tsunami deposits in high-dimensional compositional space through the use of data sets of bulk composition that have been categorised as tsunami or non-tsunami sediments. We applied this method to the 2011 Tohoku tsunami and to background marine sedimentary rocks. After an exhaustive search of all 262,144 (= 218) combinations of the 18 analysed elements, we observed several tens of combinations with discrimination rates higher than 99.0%. The analytical results show that elements such as Ca and several heavy-metal elements are important for discriminating tsunami deposits from marine sedimentary rocks. These elements are considered to reflect the formation mechanism and origin of the tsunami deposits. The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies.