EAGER: Collaborative Proposal: Probabilistic Scenarios for Megathrust Earthquakes and Tsunami Genesis

EAGER：协作提案：巨型逆冲地震和海啸成因的概率情景

• 批准号: 2136809
• 负责人: Timothy Masterlark
• 金额: $ 11.81万
• 依托单位: South Dakota School of Mines and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136809&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Proposal; Probabilistic; Scenarios

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Subduction zones, known for parallel chains of towering volcanoes and deep oceanic trenches, host Earth's most geologically complex and heavily populated regions. Subduction zones represent the continuous convergence of two tectonic plates, one of which subducts (“dives”) into the mantle while the other rides over the top of the subducting plate. Stress in this converging system continuously builds until it exceeds the frictional strength of the boundary separating the two plates. At this point, the pent-up stress is released in the form of an earthquake and warps the seafloor. This warping seafloor shifts the overlying ocean surface, a process known as tsunami genesis. Only subduction zones can generate mega-earthquakes, which can warp the seafloor over tremendously vast areas and trigger devastating tsunamis. Predicting tsunamis is a challenging problem because it requires an understanding of the inaccessible details of the stress release along the plate boundary and how it deforms the structure of the entire subduction zone. This research presents a new approach that brings the combined power of mathematics and statistics to bear on this problem. Mathematics describes the physical processes of earthquakes and tsunamis and the statistics account for what is known, or more importantly, what is unknown about the subduction zone system. Results of this research could provide the tools to evaluate risks for coastal locations that are prone to tsunamis. Numerical modeling is the key to this approach and demand for numerical modeling skills is increasing in parallel with expanding data collection initiatives and advances in computational capabilities. This project includes an educational component that will engage students from underrepresented groups in science, technology, engineering, and mathematics in formalized training in numerical modeling. This research will develop numerical tools with a probabilistic perspective to investigate the coupling of seafloor deformation from megathrust earthquakes and tsunamis. These tools will address the challenging problem of embedding sophisticated finite element models of earthquake deformation into automated Monte Carlo sampling strategies. The deformation models will have geodetically-informed slip distributions over curved fault surfaces embedded in domains having the geometric irregularities of topography and bathymetry. Domains will simultaneously account for seismic tomography and reflection models, submarine-based seafloor observations, and tsunami observations. These models will propagate uncertainties from geodetic data into probability density functions for tsunami run-up behavior along coastal locations. This research will, for the first time, quantify how the larger uncertainties for near-trench slip propagate into tsunami predictions. Finite element models are necessary to simulate the complex mechanical behavior of subduction zones and Monte Carlo sampling will reveal how uncertainties in the data and model configurations propagate into deformation and tsunami predictions. These objectives will be achieved using the well-documented 2004 Sumatra megathrust earthquake and tsunami as a case study. A short course on finite element models of earthquake deformation will be developed and delivered. The curriculum will comprise Protocol-based Modeling, Forward Modeling, Inverse Modeling, and discussions of Target Applications. Graduate students from U.S. institutions will be recruited by the Graduate Women In Science chapter and the Women in Science and Engineering program at the South Dakota School of Mines to promote participation of underrepresented students.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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。 俯冲带以平行的高耸火山链和深海海沟而闻名，这里是地球上地质最复杂、人口最稠密的地区。俯冲带代表两个构造板块的连续汇聚，其中一个板块俯冲（“潜入”）到地幔中，而另一个板块则越过俯冲板块的顶部。该会聚系统中的应力不断增加，直到超过分隔两个板的边界的摩擦强度。此时，被压抑的应力以地震的形式释放出来，使海底扭曲。这种扭曲的海底改变了上覆海洋表面，这一过程被称为海啸发生。只有俯冲带才能产生特大地震，从而使大片区域的海底扭曲并引发毁灭性的海啸。预测海啸是一个具有挑战性的问题，因为它需要了解沿着板块边界的应力释放的难以接近的细节以及它如何使整个俯冲带的结构变形。这项研究提出了一种新方法，结合数学和统计学的力量来解决这个问题。数学描述了地震和海啸的物理过程，统计数据解释了俯冲带系统的已知内容，或更重要的是，解释了未知内容。这项研究的结果可以提供评估容易发生海啸的沿海地区风险的工具。数值建模是这种方法的关键，随着数据收集计划的扩大和计算能力的进步，对数值建模技能的需求也在不断增加。该项目包括一个教育部分，将吸引来自科学、技术、工程和数学领域代表性不足群体的学生参加数值建模的正式培训。这项研究将开发具有概率视角的数值工具，以研究巨型逆冲地震和海啸造成的海底变形的耦合。这些工具将解决将复杂的地震变形有限元模型嵌入到自动蒙特卡罗采样策略中的挑战性问题。变形模型将在嵌入具有地形和测深几何不规则性的域中的弯曲断层表面上具有大地测量信息的滑移分布。域将同时考虑地震层析成像和反射模型、海底海底观测和海啸观测。这些模型将把大地测量数据的不确定性传播到沿海地区海啸爆发行为的概率密度函数中。这项研究将首次量化海沟附近滑动的较大不确定性如何传播到海啸预测中。有限元模型对于模拟俯冲带的复杂力学行为是必要的，蒙特卡罗采样将揭示数据和模型配置中的不确定性如何传播到变形和海啸预测中。这些目标将以有据可查的 2004 年苏门答腊岛巨型逆冲地震和海啸为案例研究来实现。将开发并提供有关地震变形有限元模型的短期课程。课程将包括基于协议的建模、正向建模、逆向建模以及目标应用的讨论。来自美国院校的研究生将被南达科他州矿业学院的科学女性毕业生分会和科学与工程女性项目招募，以促进代表性不足的学生的参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。