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We statistically analyzed the whole‐rock compositions of peridotites in the Oman ophiolite using a multivariate statistical technique called independent component analysis (ICA) to better understand the processes within the ophiolitic mantle section that gave rise to the compositional variations, including partial melting, mantle–melt reactions, and peridotite–fluid interactions. We found that four independent geochemical components represent most of the geochemical variations in these peridotites. We compared the independent components with petrological and geological observations, such as the mineral abundances and compositions, whole‐rock major and trace element contents, and spatial distributions of these features. We found that the four independent components (IC1–4) correspond to four processes: anhydrous partial melting at a spreading ridge, slab‐derived fluid‐fluxed melting during subduction, serpentinization, and metasomatism, respectively. The compositional variations of peridotites in the Oman ophiolite are mainly due to two mantle processes: (a) anhydrous processes at a spreading ridge (partial melting); and (b) fluid–peridotite reactions during obduction and subduction, including metasomatism and serpentinization. Plain Language Summary The Oman ophiolite is a section of oceanic lithosphere where the oceanic crust and uppermost mantle form a series of units exposed on the eastern margin of the Arabian Peninsula. We statistically analyzed the whole‐rock trace element compositions of the peridotites in the ophiolite using independent component analysis to determine the independent geochemical processes that have affected the ophiolite. Four independent components can explain the compositional variations of these peridotites. We identified four independent processes (three mantle processes and serpentinization) that occurred during the formation of the Oman ophiolite: anhydrous melting of mantle peridotite beneath a spreading ridge; fluid‐fluxed melting, metasomatism, and serpentinization as a result of reactions between peridotite and slab‐derived fluid in a subduction setting. Key Points Independent component analysis was used to identify key mantle processes by analyzing whole‐rock compositions of northern Oman ophiolite Three independent mantle processes and serpentinization account for the trace element variations in peridotites of the Fizh Massif We determined the spatial distributions of these four processes and their relationships to geological and petrological processes

We present a new and easy‐to‐use geochemical tectono‐magmatic setting discriminator to calculate the probability of membership (the Sparse Geochemical Tectono‐magmatic setting Probabilistic membershiP discriminatoR, SGTPPR) that runs in Excel. It outputs the probability of membership for eight different tectono‐magmatic settings (mid‐ocean ridge, oceanic island, oceanic plateau, continental flood basalt province, intra‐oceanic arc, continental arc, island arc, and back‐arc basin) for a given volcanic rock sample based on major and selected trace element contents (SiO2, TiO2, Al2O3, Fe2O3, MgO, CaO, K2O, Na2O, Rb, Sr, Y, Zr, Nb, and Ba). We consider all possible ratios and multiplications of these contents, in addition to the contents themselves, which improves the discrimination accuracy. We use a statistical method called sparse multinomial logistic regression to construct a robust and predictive discrimination model. By imposing the sparsity, only a small number of essential variables are included in the model. The variables are objectively extracted from 287 possible geochemical variables, including all possible ratios and multiplications of the major and trace element contents. The constructed model exhibits a high classification ability, indicating that tectonic discrimination using major and selected trace elements yields a high classification ability when ratios and multiplications are considered. The system outputs the relative weights of the variables (i.e., contents, and ratios and multiplications of contents) of the input geochemical data to the calculated membership probabilities. This information can be used to evaluate and interpret the results. We apply the model to multiple samples of a geological unit, to determine the tectonic setting. Plain Language Summary Identifying the source geochemical characteristics of a volcanic rock is essential for understanding magma generation processes and evaluating the tectonic setting of magmatism. We constructed a geochemical discriminator that runs in Excel to characterize volcanic rocks based on their chemical composition. It outputs the probability of membership to eight tectono‐magmatic settings based on the input chemical composition of a volcanic rock sample. The analysis can be conducted with major elements and commonly analyzed six trace elements, making it applicable to a wide range of samples from mafic to silicic compositions. We used a statistical method called sparse multinomial logistic regression to construct the discriminator, in which all possible ratios and multiplications of eight major and six trace element contents were considered. This system provides the relative weights of the input variables (major and trace element contents, and their ratios and multiplications) on the final results, making it easy to interpret and discuss the output. The discriminator can also be used to characterize a geological unit and volcanic body based on multiple samples, and determine its tectonic setting of formation. Key Points We present a probabilistic geochemical tectono‐magmatic setting discriminator that runs in Excel Probabilities of the memberships of a volcanic rock sample for eight different tectono‐magmatic settings can be easily computed Discrimination using major and selected trace elements yields a high classification ability when ratios and multiplications are considered

Detrital monazite geochronology has been used in provenance studies. However, there are complexities in the interpretation of age spectra due to their wide occurrence in both igneous and metamorphic rocks. We use the multinomial logistic regression (MLR) and cross-validation (CV) techniques to establish a geochemical discrimination of monazite source rocks. The elemental abundance-based geochemical discrimination was tested by selecting 16 elements from granitic and metamorphic rocks. The MLR technique revealed that light rare earth elements (REEs), Eu, and some heavy REEs are important discriminators that reflect elemental fractionation during magmatism and/or metamorphism. The best model yielded a discrimination rate of ~97%, and the CV method validated this approach. We applied the discrimination model to detrital monazites from African rivers. The detrital monazites were mostly classified as granitic and of garnet-bearing metamorphic origins; however, their proportion of metamorphic origin was smaller than the proportion that was obtained by using the elemental-ratio-based discrimination proposed by Itano et al. in Chemical Geology (2018). Considering the occurrence of metamorphic rocks in the hinterlands and the different age spectra between monazite and zircon in the same rivers, a ratio-based discrimination would be more reliable. Nevertheless, our study demonstrates the advantages of machine-learning-based approaches for the quantitative discrimination of monazite.

Large variations in the maximum earthquake magnitude (Mmax) have been observed among the world’s subduction zones. There is still no universal relationship between Mmax and a given subduction-zone parameter, such as plate age, plate dip angle, or plate velocity, which suggests that multiple parameters control Mmax. Here, we conduct exhaustive variable selections that are based on three evaluation criteria; leave-one-out cross-validation errors (LOOCVE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) to determine the combination of subduction-zone parameters that best explains Mmax. Multiple linear regression analyses are applied using 18 subduction-zone parameters as potential candidates for the explanatory variables of Mmax. The minimum BIC is obtained when five variables (trench sediment thickness, existence of an accretionary prism, upper-plate crustal thickness, bending radius of the subducting oceanic plate, and trench depth) are selected as explanatory variables; each variable contributes positively to Mmax. Minimum LOOCVE and AIC values are obtained when eight variables (the five parameters for BIC, plus the along-strike plate convergence rate, age of the subducting plate, and maximum depth of the subducting plate) are selected. Our selection of the trench sediment thickness and plate bending radius contributing to Mmax is consistent with previous studies. The results show that increasing upper-plate crustal thickness results in a large Mmax. In addition to smoothing the subducting-plate interface via subducted sediments, along-dip extension of the crustal area along the convergent plate boundary would be important for generating a large earthquake.Graphic Abstract

Kusatsu–Shirane Volcano is an active Quaternary andesitic-to-dacitic volcano located in the Central Japan Arc. We conducted a detailed petrological investigation of orthopyroxene (opx)–magnetite (mt) symplectites associated with olivine in the Sessho lava, an andesitic lava flow from Kusatsu–Shirane. We concluded that the symplectites are pseudomorphs after olivine and were formed through the breakdown of olivine in a mafic magma as a result of oxidation during mixing with a felsic magma. Various olivines and opx–mt symplectites that show different stages of the progressive breakdown reaction of olivine coexist in a single lava flow. We suggest that basaltic recharge into the magma reservoir beneath Kusatsu–Shirane occurred repeatedly, leading to a hybrid andesite magma with different types of olivine and symplectite being erupted at Kusatsu–Shirane Volcano.

Revealing subsurface structures is a fundamental task in geophysical and geological studies. Logging data are usually acquired through drilling projects, which constrain the subsurface structure, and together with the description of drill core samples, are used to distinguish geological units. Clustering is useful for interpreting logging data and making log unit classification and is usually performed by manual inspection of the data. However, the validity of clustering results with such subjective criteria may be questionable. This study proposed the application of a statistical clustering method, the hidden Markov model, to conduct unsupervised clustering of logging data. As logging data are aligned along the drilled hole, they and the geological structure hidden behind such sequential datasets can be regarded as observables and hidden states in the hidden Markov model. When log unit classification is manually conducted, depth dependency of logging data is usually focused. Therefore, we included depth information as observables to explicitly represent depth dependency of logging data. The model was applied to the following geological settings: the accretionary prism at the Nankai Trough, the onshore fault zone at the Kii Peninsula (southwest Japan), and the forearc basin at the Japan Trench. The optimum number of clusters were searched using a quantitative index. The clustering results using the hidden Markov model were consistent with previously reported classifications or lithological descriptions; however, our method allowed a more detailed division of logging data, which is useful to interpret geological structures, such as a fault or a fault zone. Therefore, the use of the hidden Markov model enabled us to clarify assumptions quantitatively and conduct clustering consistently for the entire depth range, even for different geological sites. The proposed method is expected to have wider applicability and extensibility for other types of data, including geochemical and structural geological data.

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.

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 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.

Geoneutrino observations, first achieved by KamLAND in 2005 and followed by Borexino in 2010, have accumulated statistics and improved sensitivity for more than ten years. The uncertainty of the geoneutrino flux at the surface is now reduced to a level small enough to set useful constraints on U and Th abundances in the bulk silicate earth (BSE). However, in order to make inferences on earth's compositional model, the contributions from the local crust need to be understood within a similar uncertainty. Here we develop a new method to construct a stochastic crustal composition model utilizing Bayesian inference. While the methodology has general applicability, it incorporates all the local uniqueness in its probabilistic framework. Unlike common approaches for this type of problem, our method does not depend on crustal segmentation into upper, (middle) and lower, whose classification and boundaries are not always well defined. We also develop a new modeling method to infer rock composition distributions that conserve mass balance and therefore do not bias the results. Combined with a new vast collection of geochemical data for rock samples in the Japan arc, we apply this method to geoneutrino observation at Kamioka, Japan. Currently a difficulty remains in the handling of correlations in the flux integration; we conservatively assume maximum correlation, which leads to large flux estimation errors of 60~70%. Despite the large errors, this is the first local crustal model for geoneutrino flux prediction with probabilistic error estimation in a reproducible way.

At the end of 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) was detected in Wuhan, China, that spread rapidly around the world, with severe consequences for human health and the global economy. Here, we assessed the replicative ability and pathogenesis of SARS-CoV-2 isolates in Syrian hamsters. SARS-CoV-2 isolates replicated efficiently in the lungs of hamsters, causing severe pathological lung lesions following intranasal infection. In addition, microcomputed tomographic imaging revealed severe lung injury that shared characteristics with SARS-CoV-2−infected human lung, including severe, bilateral, peripherally distributed, multilobular ground glass opacity, and regions of lung consolidation. SARS-CoV-2−infected hamsters mounted neutralizing antibody responses and were protected against subsequent rechallenge with SARS-CoV-2. Moreover, passive transfer of convalescent serum to naïve hamsters efficiently suppressed the replication of the virus in the lungs even when the serum was administrated 2 d postinfection of the serum-treated hamsters. Collectively, these findings demonstrate that this Syrian hamster model will be useful for understanding SARS-CoV-2 pathogenesis and testing vaccines and antiviral drugs.