Ferropericlase Control of Lower Mantle Rheology : Impact of Phase Morphology

Abstract The rheological properties of Earth's lower mantle play a key role for global mantle dynamics. The mineralogy of the lower mantle can be approximated as a bridgmanite‐ferropericlase mixture. Previous work has suggested that the deformation of this mixture might be dramatically affected by the large differences in viscosity between bridgmanite and ferropericlase. Here, we employ numerical models to establish a connection between ferropericlase morphology and the effective rheology of the Earth's lower mantle using a numerical‐statistical approach. Using this approach, we link the statistical properties of the two‐phase composite to its effective viscosity tensor using analytical approximations. We find that ferropericlase develops elongated structures within the bridgmanite matrix that result in significantly lowered viscosity. While our findings confirm previous endmember models that suggested a change of mantle viscosity due to the formation of interconnected weak layers, we show that significant rheological weakening can thus be already achieved even when ferropericlase does not form an interconnected network. Additionally, the alignment of weak ferropericlase leads to a pronounced viscous anisotropy that develops with total strain, which may have implications for understanding the viscosity structure of Earth's lower mantle as well as for modeling the behavior of subducting slabs. We show that to capture the effect of ferropericlase elongation on the effective viscosity tensor (and its anisotropy) in large‐scale mantle convection models, the analytical approximations that have been derived to describe the evolution of the effective viscosity of a two‐phase medium with aligned elliptical inclusions can be used

Shear properties of MgO inferred using neural networks

Shear properties of mantle minerals are vital for interpreting seismic shear wave speeds and therefore inferring the composition and dynamics of a planetary interior. Shear wave speed and elastic tensor components, from which the shear modulus can be computed, are usually measured in the laboratory mimicking the Earth's (or a planet's) internal pressure and temperature conditions. A functional form that relates the shear modulus to pressure (and temperature) is fitted to the measurements and used to interpolate within and extrapolate beyond the range covered by the data. Assuming a functional form provides prior information, and the constraints on the predicted shear modulus and its uncertainties might depend largely on the assumed prior rather than the data. In the present study, we propose a data-driven approach in which we train a neural network to learn the relationship between the pressure, temperature and shear modulus from the experimental data without prescribing a functional form a priori. We present an application to MgO, but the same approach works for any other mineral if there are sufficient data to train a neural network. At low pressures, the shear modulus of MgO is well-constrained by the data. However, our results show that different experimental results are inconsistent even at room temperature, seen as multiple peaks and diverging trends in probability density functions predicted by the network. Furthermore, although an explicit finite-strain equation mostly agrees with the likelihood predicted by the neural network, there are regions where it diverges from the range given by the networks. In those regions, it is the prior assumption of the form of the equation that provides constraints on the shear modulus regardless of how the Earth behaves (or data behave). In situations where realistic uncertainties are not reported, one can become overconfident when interpreting seismic models based on those defined equations of state. In contrast, the trained neural network provides a reasonable approximation to experimental data and quantifies the uncertainty from experimental errors, interpolation uncertainty, data sparsity and inconsistencies from different experiments.</p

Strong effect of stress on the seismic signature of the post-stishovite phase transition in the Earth’s lower mantle

The stishovite to post-stishovite phase transition may modify the scattering of seismic waves by stishovite-bearing rocks in the Earth's lower mantle. A series of continuous compression experiments on sintered polycrystalline stishovite was performed to study the effect of stress on the phase transition. The experimental results show that the phase transition shifts to lower pressures as the magnitude of deviatoric stress increases. Our results further show that the bulk modulus of sintered polycrystalline stishovite differs from that derived from single crystal measurements and decreases at the phase transition. In cold regions, such as subducted slabs, stresses may accumulate and shift the phase transition to a shallower depth. In hot regions with less stress, such as rising plumes, the phase transition is shifted to a greater depth. In addition, the phase transition may have varying seismic signatures depending on the behavior of the grain boundaries in mantle rocks and the micro-stresses present in neighboring grains

Shear properties of MgO inferred using neural networks

Shear properties of mantle minerals are vital for interpreting seismic shear wave speeds and therefore inferring the composition and dynamics of a planetary interior. Shear wave speed and elastic tensor components, from which the shear modulus can be computed, are usually measured in the laboratory mimicking the Earth's (or a planet's) internal pressure and temperature conditions. A functional form that relates the shear modulus to pressure (and temperature) is fitted to the measurements and used to interpolate within and extrapolate beyond the range covered by the data. Assuming a functional form provides prior information, and the constraints on the predicted shear modulus and its uncertainties might depend largely on the assumed prior rather than the data. In the present study, we propose a data-driven approach in which we train a neural network to learn the relationship between the pressure, temperature and shear modulus from the experimental data without prescribing a functional form a priori. We present an application to MgO, but the same approach works for any other mineral if there are sufficient data to train a neural network. At low pressures, the shear modulus of MgO is well-constrained by the data. However, our results show that different experimental results are inconsistent even at room temperature, seen as multiple peaks and diverging trends in probability density functions predicted by the network. Furthermore, although an explicit finite-strain equation mostly agrees with the likelihood predicted by the neural network, there are regions where it diverges from the range given by the networks. In those regions, it is the prior assumption of the form of the equation that provides constraints on the shear modulus regardless of how the Earth behaves (or data behave). In situations where realistic uncertainties are not reported, one can become overconfident when interpreting seismic models based on those defined equations of state. In contrast, the trained neural network provides a reasonable approximation to experimental data and quantifies the uncertainty from experimental errors, interpolation uncertainty, data sparsity and inconsistencies from different experiments

Automated pipeline processing X‐ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III

Presented and discussed here is the implementation of a software solution that provides prompt X‐ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high‐performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X‐ray diffraction techniques, e.g. large‐volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high‐resolution diffraction etc

Compressibility of ferropericlase at high-temperature: evidence for the iron spin crossover in seismic tomography

The iron spin crossover in ferropericlase, the second most abundant mineral in Earth's lower mantle, causes changes in a range of physical properties, including seismic wave velocities. Understanding the effect of temperature on the spin crossover is essential to detect its signature in seismic observations and constrain its occurrence in the mantle. Here, we report the first experimental results on the spin crossover-induced bulk modulus softening at high temperatures, derived directly from time-resolved x-ray diffraction measurements during continuous compression of (Mg0.8Fe0.2)O in a resistive-heated dynamic diamond-anvil cell. We present new theoretical calculations of the spin crossover at mantle temperatures benchmarked by the experiments. Based on our results, we create synthetic seismic tomography models to investigate the signature of the spin crossover in global seismic tomography. A tomographic filter is applied to allow for meaningful comparisons between the synthetic models and data-based seismic tomography models, like SP12RTS. A negative anomaly in the correlation between Vs variations and Vc variations (S-C correlation) is found to be the most suitable measure to detect the presence of the spin crossover in tomographic models. When including the effects of the spin crossover, the misfit between the synthetic model and SP12RTS is reduced by 63%, providing strong evidence for the presence of the spin crossover, and hence ferropericlase, in the lower mantle. Future improvement of seismic resolution may facilitate a detailed mapping of spin state using the S-C correlation, providing constraints on mantle temperatures by taking advantage of the temperature sensitivity of the spin crossover

Elastic properties of majoritic garnet inclusions in diamonds and the seismic signature of pyroxenites in the Earth's upper mantle

Majoritic garnet has been predicted to be a major component of peridotite and eclogite in Earth's deep upper mantle (&gt;250 km) and transition zone. The investigation of mineral inclusions in diamond confirms this prediction, but there is reported evidence of other majorite-bearing lithologies, intermediate between peridotitic and eclogitic, present in the mantle transition zone. If these lithologies are derived from olivine-free pyroxenites, then at mantle transition zone pressures majorite may form monomineralic or almost monomineralic garnetite layers. Since majoritic garnet is presumably the seismically fastest major phase in the lowermost upper mantle, the existence of such majorite layers might produce a detectable seismic signature. However, a test of this hypothesis is hampered by the absence of sound wave velocity measurements of majoritic garnets with relevant chemical compositions, since previous measurements have been mostly limited to synthetic majorite samples with relatively simple compositions. In an attempt to evaluate the seismic signature of a pyroxenitic garnet layer, we measured the sound wave velocities of three natural majoritic garnet inclusions in diamond by Brillouin spectroscopy at ambient conditions. The chosen natural garnets derive from depths between 220 and 470 km and are plausible candidates to have formed at the interface between peridotite and carbonated eclogite. They contain elevated amounts (12–30%) of ferric iron, possibly produced during redox reactions that form diamond from carbonate. Based on our data, we model the velocity and seismic impedance contrasts between a possible pyroxenitic garnet layer and the surrounding peridotitic mantle. For a mineral assemblage that would be stable at a depth of 350 km, the median formation depth of our samples, we found velocities in pyroxenite at ambient conditions to be higher by 1.9(6)% for shear waves and 3.3(5)% for compressional waves compared to peridotite (numbers in parentheses refer to uncertainties in the last given digit), and by 1.3(13)% for shear waves and 2.4(10)% for compressional waves compared to eclogite. As a result of increased density in the pyroxenitic layer, expected seismic impedance contrasts across the interface between the monomineralic majorite layer and the adjacent rocks are about 5–6% at the majorite-eclogite-interface and 10–12% at the majoriteperidotite-boundary. Given a large enough thickness of the garnetite layer, velocity and impedance differences of this magnitude could become seismologically detectable