Chemical interdiffusion between Na-series tephritic and phonolitic melts with different H2O content, temperature, and oxygen fugacity values

The diffusive exchange of major elements in Na-series tephrite–phonolite diffusion couples with compositions relevant to the Canary Islands magmatism was determined at 300 MPa and variable H2O concentrations (0.3 wt % to 3.3 wt %), temperatures (1150 to 1300 °C), and fO2 levels (NNO−1.5 to NNO+1.7). Composition-dependent effective binary diffusion coefficients were determined from concentration–distance profiles. Results show a wide range of diffusivities for different cations, consistently following the sequence Na Al K ≥ Mg = Fe = Ca > Si > Ti, with a mild diffusivity contrast (0.2–0.8 log units) between tephritic and phonolitic melts. Na is the fastest component, with diffusivities falling ∼ 1.0 log units above those of Si for any given condition. An anomalously fast Al diffusion is observed, with DAl falling ∼ 0.4 log units above Si and ∼ 0.6 log units below Na, suggesting a prevalence of Al–alkali coupling across our range of run conditions. The relationships between log D and H2O content in melt for all cations in an intermediate composition are strongly nonlinear and can be fitted using an exponential function with a convergence in diffusion coefficients for different temperatures with increasing H2O content. Thus, Arrhenius analyses result in a decrease in activation energies from 222–293 kJ mol−1 at 1.7 wt % H2O to 48–112 kJ mol−1 at 3.0 wt % H2O. These results provide new data on chemical interdiffusion in highly alkaline Na-rich melts and suggest that H2O content plays a key role in increasing the chemical efficiency of magma mixing at low temperatures. The obtained dataset is used to test chemical controls of magma mixing in the El Abrigo ignimbrite, Tenerife, where banded pumices involving basanitic–tephritic to phonolitic magmas are common in several compositionally bimodal ignimbrite units

Improvement of Electron Probe Microanalysis of Boron Concentration in Silicate Glasses

The determination of low boron concentrations in silicate glasses by electron probe microanalysis (EPMA) remains a significant challenge. The internal interferences from the diffraction crystal, i.e. the Mo-B4C large d-spacing layered synthetic microstructure crystal, can be thoroughly diminished by using an optimized differential mode of pulse height analysis (PHA). Although potential high-order spectral interferences from Ca, Fe, and Mn on the BKα peak can be significantly reduced by using an optimized differential mode of PHA, a quantitative calibration of the interferences is required to obtain accurate boron concentrations in silicate glasses that contain these elements. Furthermore, the first-order spectral interference from ClL-lines is so strong that they hinder reliable EPMA of boron concentrations in Cl-bearing silicate glasses. Our tests also indicate that, due to the strongly curved background shape on the high-energy side of BKα, an exponential regression is better than linear regression for estimating the on-peak background intensity based on measured off-peak background intensities. We propose that an optimal analytical setting for low boron concentrations in silicate glasses (≥0.2 wt% B2O3) would best involve a proper boron-rich glass standard, a low accelerating voltage, a high beam current, a large beam size, and a differential mode of PHA

Automated Segmentation of Olivine Phenocrysts in a Volcanic Rock Thin Section Using a Fully Convolutional Neural Network

An example of automated characterization and interpretation of the textural and compositional characteristics of solids phases in thin sections using machine learning (ML) is presented. In our study, we focus on the characterization of olivine in volcanic rocks, which is a phase that is often chemically zoned with variable Mg/(Mg + Fe) ratios, so-called magnesian number or mg#. As the olivine crystals represent only less than 10 vol% of the volcanic rock, a pre-processing step is necessary to automatically detect the phases of interest in the images on a pixel level, which is achieved using Deep Learning. A major contribution of the presented approach is to use backscattered electron (BSE) images to: 1) automatically segment all olivine crystals present in the thin section; 2) determine quantitatively their mg#; and 3) identify different populations depending on zoning type (e.g., normal vs reversal zoning) and textural characteristics (e.g., microlites vs phenocrysts). The segmentation of the olivine crystals is implemented with a pretrained fully convolutional neural network model with DeepLabV3 architecture. The model is trained to identify olivine crystals in backscattered electron images using automatically generated training data. The training data are generated automatically from images which can easily be created from X-Ray element maps. Once the olivines are identified in the BSE images, the relationship between BSE intensity value and mg# is determined using a simple regression based on a set of microprobe measurements. This learned functional relationship can then be applied to all olivine pixels of the thin section. If the highest possible map resolution (1 micron per 1 pixel) is selected for the data acquisition, the full processing time of an entire thin section of (Formula presented.) containing more than 1,500 phenocrysts and 20.000 microliths required 140 h of data acquisition (BSE + X-Ray element maps), 8 h of training and 16 h of segmentation and classification. Our further tests demonstrated that the 140 h of data acquisition can be reduced at least by a factor of 4 since only a part of the thin section area (25% or even less) needs to be used for training. The characterization of each additional thin section would only require the BSE data acquisition time (less than 48 h for a whole thin section), without an additional training step. The paper describes the training and processing in detail, shows analytical results and outlines the potential of this Deep Learning approach for petrological applications, resulting in the automatic characterization and interpretation of mineral textures and compositions with an unprecedented high resolution. Copyright © 2022 Leichter, Almeev, Wittich, Beckmann, Rottensteiner, Holtz and Sester

Influence of volatiles (H2O and CO2) on shoshonite phase equilibria

Experiments were performed at 500 MPa, 1080 °C and water activities (aH2O) from 0.0 to 1.0, in fluid-present and fluid-absent conditions, with the aim of constraining the effect of volatiles on phase equilibrium assemblages of a shoshonite from Vulcanello (Aeolian Islands, Italy). Experiments were run both under reducing and oxidizing conditions and results show that proportions, shapes and size of crystals vary as a function of the volatile composition (XH2O and XCO2) and volatile content. Clinopyroxene (Cpx) is the main crystallising phase and is compositionally analogous to Cpx crystals found in the natural rock. Plagioclase (Pl) is stable only for water activity lower than 0.1, whereas Fe–Ti oxides are present in all experimental runs, except for those where log fO2 was lower than −9, (∆NNO −0.11) irrespective of the presence of CO2. The addition of CO2 (2.8 wt%) in nominally dry experimental charges substantially reduces the crystallinity by ca. 1/3 compared to volatile free experiments. This result has important consequences upon the physical properties of the magma because it influences its viscosity and, as a consequence, velocity during its travel to the Earth surface. Assuming that the widths of Vulcanello conduits vary from 0.5 to 1.5 m, estimates of the ascent velocity vary in the range 1.5 × 10−4–3.5 × 10−2 m·s−1 for CO2 free systems and from 5.7 × 10−4–1.3 × 10−1 m·s−1 for CO2 bearing systems. Since shoshonitic magmas are common not only in the Italian volcanic provinces (Aeolian Arc, Campi Flegrei, Ischia Island, Pontine Islands, Monti Cimini, Monte Amiata, Capraia Island, Radicofani, Roccamonfina) but also in different volcanoes worldwide (Yellowstone, Mariana Arc, Kurile Arc, Tonga Arc, Andean Arc, Kamchatka Arc), we suggest that the new data will be useful to better understand shoshonitic magma behaviour under relevant geological scenarios. As such, we also suggest that hazard evaluation should incorporate the probability of very rapid ascent of poorly-evolved melts from depth

Compositional variation and 226Ra-230Th model ages of axial lavas from the southern Mid-Atlantic Ridge, 8°48′S

We present geological observations and geochemical data for the youngest volcanic features on the slow-spreading Mid-Atlantic Ridge at 8°48'S that shows seismic evidence for a thickened crust and excess magma formation. Young lava flows with high sonar reflectivity cover about 14 km2 in the axial rift and were probably erupted from two axial volcanic ridges each of about 3 km in length. Three different lava units occur along an about 11 km long portion of the ridge, and lavas from the northern axial volcanic ridge differ from those of the southern axial volcanic ridge and surrounding lava flows. Basalts from the axial rift flanks and from a pillow mound within the young flows are more incompatible element depleted than those from the young volcanic field. Lavas from this volcanic area have 226Ra-230Th disequilibria model ages of 1,000 and 4,000 years whereas the older lavas from the rift flank and the pillow mound, but also some of the lava field, are older than 8,000 years. Glasses from the northern and southern ends of the southern lava unit indicate up to 100°C cooler magma temperatures than in the center and increased assimilation of hydrothermally altered material. The compositional heterogeneity on a scale of 3 km suggests small magma batches rising vertically from the mantle to the surface without significant lateral flow and mixing. The observations on the 8°48'S lava field support the model of low frequency eruptions from single ascending magma batches that has been developed for slow-spreading ridges