CAREER: Hypothesis Testing Mantle Structure with Probabilistic Tomography

职业：用概率断层扫描检验地幔结构的假设

• 批准号: 2237813
• 负责人: Scott Burdick
• 金额: $ 63.25万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237813&HistoricalAwards=false
• 关键词: CAREER; Hypothesis; Testing; Mantle; Structure

Seismic tomography, which uses waves produced by earthquakes to create 3D models of deep Earth structure, is an indispensable tool for understanding the Earth's mantle. Tomographic models are used to address questions about Earth’s tectonic past, geodynamics, and the processes underlying natural hazards, in addition to helping locate earthquakes and enforce test ban treaties. However, due to difficulty in estimating complex model uncertainties, these questions are met largely with qualitative analysis. In order to move towards quantitative hypothesis testing of mantle structure, this CAREER project will apply a probabilistic framework to estimate the full range of Earth models that agree with seismic data and other constraints. These models of seismic properties will be constructed using large earthquake datasets, first for the mantle beneath Alaska, then the entire globe, and novel analyses will be applied to answer geologic and geodynamic questions. A new undergraduate team research course will apply the principles espoused by this project to environmental problems in Detroit, MI. Students at different experience levels will form teams to identify targets, define testable hypotheses, and design geophysical surveys to assess them. Research results will also be incorporated into two planetarium shows, one on the geologic history of Michigan and one on natural hazards, to be presented regularly at the Wayne State Planetarium to K-12 schools, undergraduate classes, and the public.The project aims to create new mantle models with fully quantified uncertainty and forward a framework to compare model probabilities to predictions made by geology and geodynamics. Research activities will be motivated by four major questions: 1) How well do body wave data constrain mantle velocity variations? 2) To what extent do hypotheses posed by plate reconstructions and sinking rates agree with seismic structure? 3) Which mantle convection models comport with the scale and distribution of heterogeneity? 4) Which data sources, seismic phases, and prior information best resolve mantle properties? These questions will be addressed by employing Transdimensional Bayesian Inference (TBI) methods to create global and regional ensembles of mantle P- and S-wave velocity models and transition zone thickness and amplitude. Ensembles will be evaluated using novel analyses, resulting in constraints on global slab properties, estimates of the existence and geometry of plumes, and insights on plate reconstructions. Assessment of the upper mantle will be bolstered by a new TBI method for transition zone topography using machine learning-picked precursor phases. Research activities will produce efficient GPU-accelerated software for 3D TBI tomography, new methods for picking transition zone-reflected phases and inferring phase transition topography, and approaches for incorporating TBI results as prior information. Practices for interrogating, visualizing, and sharing high-dimensional ensembles and posterior distributions will be defined.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences, and by the Division of Earth Sciences program in Education and Human Resources.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.

地震层析成像利用地震产生的波来创建地球深层结构的3D模型，是了解地幔不可或缺的工具。层析成像模型除了帮助定位地震和执行禁止核试验条约外，还用于解决有关地球构造历史、地球动力学和潜在自然灾害过程的问题。然而，由于很难估计复杂的模型不确定性，这些问题在很大程度上是定性分析。为了走向地幔结构的定量假设检验，这个职业项目将应用一个概率框架来估计与地震数据和其他约束条件相一致的各种地球模型。这些地震属性模型将使用大型地震数据集构建，首先是阿拉斯加地幔，然后是整个地球，并将应用新的分析来回答地质和地球动力学问题。一门新的本科团队研究课程将把这个项目所支持的原则应用到密歇根州底特律的环境问题上。不同经验水平的学生将组成团队来确定目标，定义可检验的假设，并设计地球物理调查来评估它们。研究成果还将被纳入两个天文馆展览，一个是关于密歇根州地质史的，另一个是关于自然灾害的，将在韦恩州立天文馆定期向K-12学校、本科生班级和公众展示。该项目旨在创建具有完全量化不确定性的新地幔模型，并提出一个框架，将模型概率与地质学和地球动力学做出的预测进行比较。研究活动将受到四个主要问题的推动：1)体波数据对地幔速度变化的约束程度如何？2)板块重建和下沉速率提出的假设与地震结构的一致性有多大？3)哪些地幔对流模型符合非均质性的规模和分布？4)哪些数据源、地震相和先验信息最能解决地幔性质？这些问题将通过使用跨维贝叶斯推理方法来解决，以创建全球和区域地幔P波和S波速度模型以及过渡带厚度和幅度的系综。将使用新的分析对整体进行评估，产生对全球板块属性的限制，对羽流的存在和几何的估计，以及对板块重建的洞察。对上地幔的评估将得到一种使用机器学习挑选的前驱阶段的过渡区地形的新的TBI方法的支持。研究活动将产生用于3D TBI断层成像的高效GPU加速软件、拾取过渡区反射相和推断相变地形的新方法，以及将TBI结果作为先验信息纳入的方法。将定义询问、可视化和共享高维集合和后验分布的实践。该项目由地球科学局共同资助，以支持地学领域的AI/ML进步，并由地球科学教育和人力资源部计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。