Parallel Semiclassical Methods for Seismic Wave Propagation, Inversion, and Data Analysis

地震波传播、反演和数据分析的并行半经典方法

• 批准号: 1818592
• 负责人: Xu Yang
• 金额: $ 19.99万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818592&HistoricalAwards=false
• 关键词: Parallel; Semiclassical; Methods; Seismic; Wave

Predicting seismic events, locating hidden resources and deepening our understanding of our planet's internal complexity require overcoming challenges from multi-interdisciplinary sciences including mathematics and geophysics. The proposed research will initiate projects to develop parallel semiclassical methods for seismic wave propagation, inversion and data analysis, with results computed, visualized and explored to create three-dimensional imaging of inner Earth structure. Specifically, the projects include development of the frozen Gaussian approximation (FGA) to handle elastic wave propagation in high-contrast, heterogeneous media, inversion of background velocity using full waveform inversion by constructing a surrogate model based on FGA to enlarge the search-function spaces used in the particle swarm method, and application of developed mathematical theory and associated computer programs to collected seismic data. The projects will develop semiclassical approaches for seismic wave propagation, inversion and data analysis, leading to quantitative imaging of our Earth's whole inner structure, and provide deep understanding of seismic activities. The research introduces a mathematically systematic way of attacking requests on reducing time of simulation in seismic tomography and inversion processes. More broadly, these new methods and models will provide fundamental advances in computational mathematics, leading to practical benefit in the field of geophysics, which plays such significant roles in responsible use of resources. The expected results will find applications in other emerging science and engineering fields such as spin-transfer torque in materials science and E. coli chemotaxis in biology, some of the problems the PI has also been working on.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.

预测地震事件，定位隐藏的资源和加深我们对地球内部复杂性的理解需要克服包括数学和物理学在内的多学科科学的挑战。拟议的研究将启动项目，开发地震波传播、反演和数据分析的平行半经典方法，计算、可视化和探索结果，以创建地球内部结构的三维成像。具体而言，这些项目包括开发冻结高斯近似（FGA）来处理高对比度非均匀介质中的弹性波传播，通过构建基于FGA的代理模型来扩大粒子群方法中使用的搜索函数空间，使用全波形反演来反演背景速度，以及将开发的数学理论和相关计算机程序应用于收集的地震数据。这些项目将发展地震波传播、反演和数据分析的半经典方法，从而对我们地球的整个内部结构进行定量成像，并提供对地震活动的深入了解。该研究介绍了一种数学系统的方法来攻击减少地震层析成像和反演过程中的模拟时间的要求。更广泛地说，这些新方法和模型将为计算数学提供根本性的进步，从而在负责任地使用资源方面发挥重要作用的物理学领域带来实际利益。预期的结果将在其他新兴的科学和工程领域中找到应用，如材料科学中的自旋转移力矩和E。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

项目成果

Deep unfitted Nitsche method for elliptic interface problems

• DOI: 10.4208/cicp.oa-2021-0201
• 发表时间: 2021-07
• 期刊: ArXiv
• 作者: Hailong Guo;Xu Yang

Frozen Gaussian Approximation for the Dirac Equation in Semiclassical Regime

• DOI: 10.1137/18m1222831
• 发表时间: 2019-10
• 期刊: SIAM J. Numer. Anal.
• 作者: L. Chai;E. Lorin;Xu Yang

Unfitted Nitsche’s Method for Computing Wave Modes in Topological Materials

• DOI: 10.1007/s10915-021-01540-w
• 发表时间: 2019-08
• 期刊: Journal of Scientific Computing
• 影响因子: 2.5
• 作者: Hailong Guo;Xu Yang;Yi Zhu

Frozen Gaussian approximation for 3-D elastic wave equation and seismic tomography

• DOI: 10.1093/gji/ggy498
• 发表时间: 2018-10
• 期刊: Geophysical Journal International
• 影响因子: 2.8
• 作者: J. Hateley;L. Chai;P. Tong;Xu Yang

Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

• DOI: 10.1029/2021jb023830
• 发表时间: 2021-12
• 期刊: Journal of Geophysical Research: Solid Earth
• 作者: Wen Ding;Tianjue Li;Xu Yang;Kui Ren;P. Tong