CDI Type II/Collaborative Research: Ultra-high Resolution Dynamic Earth Models through Joint Inversion of Seismic and Geodynamic Data

将利用地震和地球动力学观测数据，通过求解由高分辨率地幔对流和地震波传播模型控制的联合非线性逆问题，推断统一的动力地球模型。这将导致用于约束板块构造和地幔对流的各种数据的合并，提供过去5000万年来地球表面和内部的新4-D图像。反问题需要严重的数学，计算和地球物理的挑战，传统的方法是无法解决的。 该项目有几个部分计划克服这些挑战。 我们将设计反演方法，通过创建算法来解决耦合的全波形地震和地球动力学反演问题，可以从大量的地震和地球动力学数据中提取全部信息，并使用这些方法从宽带地震和地球动力学数据中反演全球和区域地球模型。 我们将开发可以扩展到大量的CPU核心和复杂的存储器层次结构表征新兴的多petaflop系统的反演算法。 我们还将扩展自适应网格细化（AMR）的想法，从大规模的正演模拟的大规模逆问题的设置。除了科学影响之外，该项目还有一个推广和教育计划，其重点是传播动态地球模型的4D动画。地球是一个四维动力学系统，地幔对流驱动着板块构造和大陆漂移，反过来又控制着从地震和火山到造山运动和长期海平面变化的许多活动。 尽管地幔对流在我们对地球的理解中扮演着核心角色，但我们的知识中存在巨大的一级差距，问题就像板块构造的主要驱动力和阻力一样基本，什么是整个星球的能量平衡。 然而，地球物理数据量的快速增长，千万亿次计算时代的到来，以及高分辨率正演模型模拟能力的出现，现在提供了一个机会，将地球物理数据合并到动态地球模型中，以大大提高我们对地球结构的理解。 该项目可能会促进地球动力学领域的转变，因为它将导致从采用高分辨率正演模型的数据中严格推断地球模型。 此外，通过开发大规模反问题的并行网格算法、大规模非线性反问题的可扩展方法以及大型复杂多物理场数据联合反演的反方法，该项目可以为许多具有类似需求的其他领域带来变革。正演模型。所有这些计算/数学方面的进步将使更广泛的科学家群体受益，他们所研究的问题比本项目中遇到的问题要广泛得多。

Seismic and geodynamic observational data will be employed to infer a unified dynamic earth model through solution of the joint nonlinear inverse problem governed by high-resolution mantle convection and seismic wave propagation models. This will lead to a merging of the diverse data used to constrain plate tectonics and mantle convection, providing a new 4-D picture of earth's surface and interior over the last 50 million years. The inverse problem entails severe mathematical, computational, and geophysical challenges, which conventional methods are incapable of addressing. There are several parts of this project that are planned to overcome these challenges. We will devise inverse methods that can extract full information from the large volumes of seismic and geodynamic data by creating algorithms to solve the coupled full waveform seismic and geodynamic inverse problem, and use those methods to invert for global and regional earth models from broadband seismic and geodynamic data. We will develop inversion algorithms that can scale to the large numbers of CPU cores and complex memory hierarchies characterizing emerging multi-petaflop systems. We will also extend adaptive mesh refinement (AMR) ideas from large-scale forward simulation to the setting of large-scale inverse problems. Beyond the scientific impact, the project has a program of outreach and education that is highlighted by dissemination of 4-D animations of dynamic earth models. This project fits within the "From Data to Knowledge" and "Understanding Complexity" themes of the CDI Program.The earth is a four-dimensional dynamic system where mantle convection drives plate tectonics and continental drift and, in turn, controls much activity ranging from the occurrence of earthquakes and volcanoes to mountain building and long-term sea level change. Despite the central role mantle convection plays in our understanding of earth, we have enormous first-order gaps in our knowledge, with questions that are as basic as what are the principal driving and resisting forces on plate tectonics to what is the energy balance of the planet as a whole. However, rapidly-expanding volumes of geophysical data, the arrival of the petaflop computing era, and the emergence of high-resolution forward model simulation capabilities now provide an opportunity to merge the geophysical data into dynamic earth models to greatly enhance our understanding of earth structure. This project could catalyze a shift in the field of geodynamics, since it will lead to rigorous inference of earth models from data employing high-resolution forward models. Moreover, the project could be transformative for many other fields with similar needs, through the development of parallel mesh algorithms for large-scale inverse problems, scalable methods for large-scale nonlinear inverse problems, and inverse methods for joint inversion of data for large complex multi-physics forward models. All of these computational/mathematical advances will benefit a much wider community of scientists working on a much broader set of problems than the ones encountered in this project.