Multiple resolution surface wave tomography: the Mediterranean basin

From a large set of fundamental-mode surface wave phase velocity observations, we map the transversely isotropic lateral heterogeneities in the upper-mantle shear velocity structure. We design a multiple resolution inversion procedure, which allows us to parametrize any selected region more finely than the rest of the globe. We choose, as a high-resolution region, the upper mantle underlying the Mediterranean basin. We formulate the inverse problem as in a previous paper by Boschi & Ekström, calculating regional JWKB (Jeffreys-Wentzel-Kramers-Brillouin) surface wave sensitivity kernels for each pixel of a 2°× 2° starting model, including the high-resolution global crustal map Crust 2.0. We find that the available surface wave data can resolve the most important geophysical features of the region of interest, providing a reliable image of intermediate spatial wavelengt

Arrival-angle anomalies across the USArray Transportable Array

We construct composite maps of surface-wave arrival-angle anomalies using clustered earthquakes and an array method for measuring wave-front geometry. This results in observations of arrival angles covering the entire footprint of the USArray Transportable Array during 2006–2010. Bands of arrival-angle deviations in the propagation direction indicate the presence of heterogeneous velocity structure both inside and outside of the array. We compare the observed patterns to arrival angles predicted using two global tomographic models, the mantle model S362ANI and the surface-wave-dispersion model GDM52. We use both ray-theory-based prediction methods and measurements on synthetic data calculated using a spectral-element method. Both models and all prediction methods produce similar mean arrival angles and long-wavelength patterns of anomalies which are similar to the observations. Predicted short-wavelength features generally do not agree with the observations. The spectral-element method produces some complexity that is not obtained using the ray-theory-based methods; this predicted complexity is similar in character to the observed patterns, but does not match them

First-principles calculations of the lattice thermal conductivity of the lower mantle

The temperature variations on top of the core-mantle boundary are governed by the thermal conductivity of the minerals that comprise the overlying mantle. Estimates of the thermal conductivity of the most abundant phase, MgSiO3 perovskite, at core-mantle boundary conditions vary by a factor of ten. We performed ab initio simulations to determine the lattice thermal conductivity of MgSiO3 perovskite, finding a value of 6.8 ± 0.9 W m-1 K-1 at core-mantle boundary conditions (136 GPa and 4000 K), consistent with geophysical constraints for the thermal state at the base of the mantle. Thermal conductivity depends strongly on pressure, explaining the dynamical stability of super-plumes. The dependence on temperature and composition is weak in the deep mantle: our results exhibit saturation as the phonon mean free path approaches the interatomic spacing. Combining our results with seismic tomography, we find large lateral variations in the heat-flux from the core that have important implications for core dynamics

In-situ measurement of texture development rate in CaIrO₃ post-perovskite

The rate of crystallographic preferred orientation (CPO) development during deformation of post-perovskite is crucial in interpreting seismic anisotropy in the lowermost mantle but the stability field of MgSiO3 post-perovskite prevents high-strain deformation experiments being performed on it. Therefore, to constrain the rate of CPO development in post-perovskite, we deformed CaIrO3, a low-pressure analogue of MgSiO3 post-perovskite, in simple shear at 3.2GPa and 400○C to a shear strain (γ) of 0.81. From X-ray diffraction patterns acquired during deformation, we invert for CPO as a function of strain. By comparing the CPO that develops with visco-plastic self-consistent (VPSC) models we constrain the critical resolved shear stresses (CRSS) of the non-primary slip-systems in CaIrO3 to be of order 6 times stronger than the primary [100](010) slip system. This value is significantly less than has been assumed by previous studies and if applicable to MgSiO3 implies that seismic anisotropy in the D′ layer develops slower than has previously been assumed

Mantle flow in regions of complex tectonics: insights from Indonesia

Indonesia is arguably one of the tectonically most complex regions on Earth today due to its location at the junction of several major tectonic plates and its long history of collision and accretion. It is thus an ideal location to study the interaction between subducting plates and mantle convection. Seismic anisotropy can serve as a diagnostic tool for identifying various subsurface deformational processes, such as mantle flow, for example. Here, we present novel shear wave splitting results across the Indonesian region. Using three different shear phases (local S, SKS, and downgoing S) to improve spatial resolution of anisotropic fabrics allows us to distinguish several deformational features. For example, the block rotation history of Borneo is reflected in coast-parallel fast directions, which we attribute to fossil anisotropy. Furthermore, we are able to unravel the mantle flow pattern in the Sulawesi and Banda region: We detect toroidal flow around the Celebes Sea slab, oblique corner flow in the Banda wedge, and sub-slab mantle flow around the arcuate Banda slab. We present evidence for deep, sub-520 km anisotropy at the Java subduction zone. In the Sumatran backarc, we measure trench-perpendicular fast orientations, which we assume to be due to mantle flow beneath the overriding Eurasian plate. These observations will allow to test ideas of, for example, slab–mantle coupling in subduction regions

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required