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.

地震层析成像技术利用地震产生的波来创建地球深层结构的 3D 模型，是了解地幔的不可或缺的工具。层析成像模型可用于解决有关地球构造过去、地球动力学和自然灾害潜在过程的问题。然而，由于难以估计复杂模型的不确定性，这些问题主要通过定性分析来解决，以便进行假设检验。为了研究地幔结构，该 CAREER 项目将应用概率框架来估计与地震数据和其他约束一致的全方位地球模型，这些地震特性模型将使用大型地震数据集构建，首先是阿拉斯加下方的地幔，然后是。一个新的本科团队研究课程将应用该项目所倡导的原理来解决密歇根州底特律的环境问题，并采用新颖的分析来回答地质和地球动力学问题。 ,定义可检验的假设，并设计地球物理调查来评估它们。研究结果还将纳入两场天文馆展览，一场是关于密歇根州的地质历史，另一场是关于自然灾害，将定期在韦恩州立天文馆向 K-12 学校展示。该项目旨在创建具有完全量化不确定性的新地幔模型，并提出一个将模型概率与地质学和地球动力学预测进行比较的框架。研究活动将受到四个主要问题的推动：1）如何进行。体波数据是否限制了地幔速度的变化？ 2） 板块重建和下沉速率提出的假设在多大程度上与地震结构相符？ 3） 哪些地幔对流模型符合非均质性的规模和分布？地震相位和先验信息可以最好地解决地幔属性吗？这些问题将通过采用跨维贝叶斯推理 (TBI) 方法创建全球和区域地幔纵波和横波速度模型来解决。新的 TBI 将支持对整体板片特性的限制、羽流的存在和几何形状的估计以及对上地幔重建的评估。使用机器学习选取的前体相进行过渡区形貌的方法研究活动将开发用于 3D TBI 断层扫描的 GPU 加速软件、拾取过渡区反射相和推断相变形貌的新方法以及合并方法。 TBI 结果作为先验信息将被定义为询问、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和该部门共同资助，以支持地球科学领域的人工智能/机器学习进步。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。