Collaborative Research: Wave Equation Tomography and Data Assimilation: A New Approach to Estimating P and S Speed Variations in Earth's Lower Mantle

合作研究：波动方程断层扫描和数据同化：估计地球下地幔 P 和 S 速度变化的新方法

• 批准号: 0630493
• 负责人: Maarten de Hoop
• 金额: $ 2.94万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-20 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0630493&HistoricalAwards=false
• 关键词: Collaborative; Research; Wave; Equation; Tomography

The Principal Investigators seek funding for collaborative research that aims to develop a new class of tomographic model by explicit integration of observational, theoretical, and computational aspects of seismic data analysis and interpretation. This effort extends their previous work in tomography and involves the measurement of new data sets and the development of wave propagation theory and multi-grid technology that allowsjoint interpretation of data with different sensitivities to Earth's structure. Quantitative integration of thesecomponents is, in our view, a viable and essential step toward the accurate mapping of spatial variations in elastic properties, temperature, and composition. Here the PI's will focus on relative variations in VP and VS in the bottom ~1000 km of Earth's lower mantle, which, they believe, contains critical clues tounderstanding mantle convection and Earth's thermo-chemical evolution over geological time.Intellectual Merit: Over the past decades global tomography has produced spectacular images of, for instance, mantle flow trajectories and structural complexity near the base of the mantle. However, uneven data coverage and heterogeneous data quality render non-unique, fuzzy images, with substantial spatial variations in reliability. Regularization and the use of inaccurate wave propagation theory probably produce incorrect estimates of elastic parameters even where sampling seems adequate, and the magnitude of wavespeed variations is usually poorly constrained. Moreover, results based on different data sets often disagree in important aspects, and correct joint interpretation of data with different sensitivities to Earth's structure (e.g., body- and surface waves, P or S waves measured at different frequencies) remains a major challenge. The approximate nature of the "red and blue" images impedes quantitative interpretation and integration with other geophysical constraints and keeps tomography from reaching its full potential as a quantitative probe of Earth's deep interior. This the PI's seek to change. For better parameter estimation they need to exploit the richness of broad-band waveforms and they need more powerful theoretical frameworks for integration and joint interpretation of diverse data sets. The ultimate objective of our approach toward multi-resolution data fusion for global tomography is to produce better 3-D models of Earth's deep interior - on a range of length scales and from a variety of seismological data - by improving (and explicitly linking) three essential aspects of imaging: Data quality and coverage: using automated procedures and multi-resolution concepts (such as time frequency wavelets) they will enhance spatial and spectral data coverage by extracting phase velocity and arrival time information from the vast number of waveforms available through international data centers. Wave propagation theory: recognizing the need to account for (and benefit from) the different sampling properties of the data considered, and inspired by recent advances in understanding finite frequency effects, they will compute accurate sensitivity kernels for the back-projection of the newly measured data. Parameterization and regularization: to preserve and exploit the localization properties of 3-D sensitivity kernels we will use adaptive multi-grid parameterization and regularization techniques for joint inversion.The research proposed here focuses on (i) measuring teleseismic P and S type body-wave travel times, (ii) inversion for 3-D variations in .lnVS/.lnVP (or related parameters) in Earth's mantle, and (iii) refining - or refuting - existing views on compositional heterogeneity in the lowermost mantle. They can build on experience in observational seismology and tomography (Van der Hilst, MIT) and wave propagation and inversion theory (De Hoop, CSM), and for the automated data processing they will collaborate with Ritsema (IPGP, France) and involve a postdoctoral associate (for which some fund matching is sought). Broader Impact: Along with mineral physics data, accurate estimates of elastic parameters are needed to constrain spatial variations in compositon and temperature and, thus, models of mantle dynamics and mineralogy. Furthermore, the concept of and tools for data fusion developed here prepare for the handling and interpretation of large data sets of USARRAY data. The proposed work constitutes the first part of a PhD project at MIT, but students at MIT and CSM will be involved in aspects of the research,either as a Undergraduate Research OPportunity (UROP) or in fulfillment of General Exam requirements.

首席研究人员寻求资金用于合作研究，旨在通过明确整合地震数据分析和解释的观测、理论和计算方面来开发一种新的层析模型。这项工作扩展了他们以前在层析成像方面的工作，涉及新数据集的测量以及波传播理论和多重网格技术的发展，从而能够对对地球结构具有不同敏感性的数据进行联合解释。在我们看来，这些成分的定量集成是精确绘制弹性性能、温度和组成的空间变化图的可行和必要的一步。在这里，PI将专注于地球下地幔底部~1000公里处Vp和Vs的相对变化，他们认为，这些变化包含了了解地幔对流和地球在地质时期的热化学演化的关键线索。智力优势：在过去的几十年里，全球层析成像已经产生了壮观的图像，例如，地幔底部附近的地幔流动轨迹和结构复杂性。然而，不均匀的数据覆盖和不同的数据质量导致不唯一的、模糊的图像，在可靠性方面具有很大的空间差异。正则化和不准确的波传播理论的使用可能会产生对弹性参数的错误估计，即使在采样似乎足够的情况下，波速变化的幅度通常也不受很好的约束。此外，基于不同数据集的结果往往在重要方面不一致，正确地联合解释对地球结构具有不同敏感性的数据(例如，以不同频率测量的体波和面波、P波或S波)仍然是一个重大挑战。红色和蓝色图像的近似性质阻碍了定量解释和与其他地球物理约束的整合，并使层析成像无法充分发挥其作为地球深处定量探测的潜力。这是公安党寻求改变的。为了更好地估计参数，他们需要利用丰富的宽带波形，他们需要更强大的理论框架来整合和联合解释不同的数据集。我们的全球层析成像多分辨率数据融合方法的最终目标是通过改进(并明确连接)成像的三个基本方面：数据质量和覆盖范围：使用自动化程序和多分辨率概念(如时间频率小波)，从国际数据中心提供的大量波形中提取相速度和到达时间信息，从而产生更好的地球深部三维模型。波传播理论：他们认识到需要考虑所考虑的数据的不同采样特性(并从中受益)，并受到最近在理解有限频率效应方面的进展的启发，将为新测量的数据的反投影计算准确的灵敏度核。参数化和正则化：为了保持和利用三维敏感核的局部化特性，我们将使用自适应多重网格参数化和正则化技术进行联合反演。本文的研究集中在(I)测量远震P和S类型的体波旅行时，(Ii)反演地幔中.lnVS/.lnVp(或相关参数)的三维变化，以及(Iii)改进或驳斥现有的关于地幔下部成分不均质性的观点。他们可以在观测地震学和层析成像(麻省理工学院范德希尔斯特)和波传播和反演理论(德霍普，CSM)的经验基础上发展，对于自动化数据处理，他们将与RitSema(IPGP，法国)合作，并涉及一名博士后助理(正在寻求一些资金匹配)。更广泛的影响：除了矿物物理数据，需要精确估计弹性参数，以限制成分和温度的空间变化，从而限制地幔动力学和矿物学模型。此外，这里开发的数据融合的概念和工具为处理和解释美国阵列数据的大数据集做好了准备。这项拟议的工作是麻省理工学院博士项目的第一部分，但麻省理工学院和CSM的学生将参与研究的各个方面，要么是作为本科生研究机会(UROP)，要么是为了满足普通考试要求。