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

地幔的热对流驱动着板块构造。它是人类面临诸多风险(如地震、火山喷发、海啸)的根源。这一过程吸收了地球内部的热量，特别是其核心结晶产生的热量。核心半径约3500公里(约2200英里)，主要由铁和少量镍组成。它的液态外壳，也就是外核，产生地球的磁场。在地核上方是岩石地幔，这是包裹在地球外壳中的一层热层，主要是固体硅酸盐。核幔边界位于地球表面以下约2900公里(1800英里)处。这是一条复杂而关键的边界。在那里，从地核到地幔的热传递限制了地球发电机，并增强了地幔对流。通过CMB上方的精细地震成像，观察到了深部地幔流动的模式。这些构造在矿物学和热力学状态方面仍对解释提出挑战。在这里，研究人员关注的是地幔系统。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专家组成。它引入了创新的方法来分析地幔结构的起源，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普遍存在的极端压力和温度下获得的。渐渐地，科学家们揭开了深部地幔结构的起源、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。该项目还为一名职业生涯早期的女科学家提供支持，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。在此，研究人员使用了由全球伴随层析成像获得的最新横波(S波)和纵波(P波)模型，而没有参考一维球面模型或假设纵波(Vp)和横波速度(VS)不均匀之间的相关性。他们还使用直接反演、机器学习算法和最新的矿物物理结果，对经历铁自旋交叉(ISC)的矿物相的热弹性性质进行了研究。他们特别注意ISC的影响，它破坏了由横向温度或组成变化引起的VS和Vp异质性之间的通常相关性。它们集中在下地幔结构上，主要是植根于CMB的羽流，可能是该地区的板块。在这个过程中，他们正在对ISCS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，他们就可以运行地球动力学模拟，以了解ISC对地幔动力学的影响。相反，地球动力学模拟的结果与热弹性数据相结合，用于合成层析图像，以便与观测到的地幔结构进行比较。该项目所产生的技术诀窍，即方法、软件、数据库和结果，将通过同行评议的期刊和专门的网站，如Burnman、Perple_X、IRIS、githorb提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.