CSEDI Collaborative Research: Understanding what we see in the lower mantle - mineral physics interpretation of seismic tomographic images

地幔热对流驱动板块构造。它是人口众多风险的根源（例如，地震、火山爆发、海啸）。这个过程提取了地球内部的热量，特别是由其核心的结晶产生的热量。 核心半径约3，500公里（约2，200英里），主要由铁和一些镍组成。 它的液态外壳，即外核，产生了地球的磁场。地核上方是岩石地幔，这是一层包裹在地壳中的高温固体硅酸盐。 核幔边界（CMB）位于地球表面以下约2，900公里（1800英里）处。这是一个复杂而关键的边界。在那里，从地核到地幔的热传递限制了地球发电机，并为地幔对流提供了动力。地幔流动的深部模式被观察到以上CMB的精细地震成像。这些结构仍然挑战矿物学和结晶状态方面的解释。在这里，研究人员专注于地幔系统。计算科学家的多学科团队由一名矿物物理学家，两名地震学家，一名应用数学家和一名地球动力学家组成。它介绍了分析地幔结构起源的创新方法，包括机器学习算法。这些模型受到最新矿物物理数据的约束，这些数据是在地球内部的极端压力和温度下获得的。渐渐地，科学家们揭开了深层地幔结构的起源，成分和温度。该项目的成果，即，最先进的方法、软件和数据库将使地球科学界受益。该项目还为一名早期职业女性科学家提供支持，并为哥伦比亚大学和普林斯顿大学的四名研究生提供培训。在这里，研究人员使用通过全球伴随层析成像获得的最新剪切（S-）和压缩（P-）波模型，而不参考1维球形模型或压缩（VP）和剪切速度（VS）不均匀性之间的相关性假设。他们还使用直接反演，机器学习算法和最新的矿物物理学结果进行铁自旋交叉（ISC）的矿物相的热弹性性质。他们特别注意ISC的影响，这破坏了通常的相关性VS和VP的横向温度或成分变化所造成的不均匀性。他们专注于下地幔结构，主要是植根于CMB的地幔柱，并可能在该地区的板块。在此过程中，他们正在对ISC上的矿物物理数据进行格式化，以便通过两个流行的热化学和热弹性软件/数据库框架- BurnMan和Perple_X -与地球动力学代码结合使用。有了这个软件/数据基础设施，他们运行地球动力学模拟，以了解ISC对地幔动力学的影响。相反，耦合到热弹性数据的地球动力学建模的结果被用来合成断层图像与观察到的地幔结构进行比较。该项目产生的专有技术，即，方法、软件、数据库和结果将通过同行评审期刊和专门网站提供，例如，BurnMan，Perple_X，IRIS，github.该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Earth’s mantle thermal convection drives plate tectonics. It is at the origin of numerous risks for populations (e.g., earthquakes, volcanic eruptions, tsunamis). This process extracts Earth’s internal heat, notably produced by the crystallization of its core. The core is ~3,500 km (~2,200 mi) in radius and consists mostly of iron with some nickel. Its liquid outer shell, the outer core, generates the Earth’s magnetic field. Above the core lies the rocky mantle, a hot layer of mostly solid silicates wrapped into the planet’s crust. The core-mantle boundary (CMB) is located ~2,900 km (1800 mi) beneath the Earth’s surface. It is a complex and critical boundary. There, heat transfer, from the core to the mantle, constrains the geodynamo and powers mantle convection. Deep patterns of mantle flow are observed by refined seismic imaging above the CMB. These structures still challenge interpretations in terms of mineralogy and thermodynamical state. Here, researchers focus on the mantle system. The multidisciplinary team of computational scientists consists of a mineral physicist, two seismologists, an applied mathematician, and a geodynamicist. It introduces innovative approaches to analyze the origin of mantle structures, including machine learning algorithm. The models are constrained with the latest mineral physics data, obtained at the extreme pressures and temperatures prevailing in Earth’s interior. Gradually, the scientists unveil the origins, compositions, and temperatures of the deep mantle structures. Outcomes of the project, i.e., state-of-the-art methods, software, and databases, will benefit the Earth Science community. The project also provides support for an early career female scientist, and training for four graduate students at Columbia University and Princeton University.Here, the researchers use the latest shear (S-) and compressional (P-) wave models obtained by global adjoint tomography, without reference to a 1D spherical model or assumptions of correlations between compressional (VP) and shear velocity (VS) heterogeneities. They also use direct inversion, machine learning algorithms, and the latest mineral physics results on thermoelastic properties of mineral phases undergoing iron spin crossover (ISC). They pay particular attention to the effect of the ISC which disrupts the usual correlation between VS and VP heterogeneities caused by lateral temperature or composition variations. They focus on lower mantle structures, mainly plumes rooted at the CMB and possibly slabs in this region. In the process, they are formatting the mineral physics data on ISCs to make it available through two popular thermochemical and thermoelasticity software/database frameworks – BurnMan and Perple_X – that couple with geodynamic codes. With this software/data infrastructure in place, they run geodynamic simulations to understand the effect of ISC on mantle dynamics. Conversely, results of geodynamic modeling coupled to thermoelasticity data are used to synthesize tomographic images to be compared with observed mantle structures. The know-how generated by this project, i.e., methods, software, databases, and results will be made available through peer-reviewed journals and in specialized web sites, e.g., BurnMan, Perple_X, IRIS, github.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.