CAREER: Physics-Informed Deep Learning for Understanding Earthquake Slip Complexity

职业：基于物理的深度学习用于理解地震滑动的复杂性

• 批准号: 2339996
• 负责人: Brittany Erickson
• 金额: $ 71.04万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339996&HistoricalAwards=false
• 关键词: CAREER; Physics; Informed; Deep; Learning

What it is about one fault that causes it to slip suddenly, unleashing catastrophic earthquakes, while another just creeps along steadily or produces smaller, more frequent earthquakes? This is difficult to assess because faults cannot be directly observed at depths where earthquakes start, typically 5 to 15 miles below ground. We must rely instead on indirect measurements made by instruments at the Earth's surface, and computer models representing the fault and how it slips in response to pressures deep in the Earth. Properties of the virtual fault and surrounding rock can be repeatedly adjusted until the model outputs data that closely match real-world observations from seismometers and other instruments. This process is slow and expensive, even when scientists use clever strategies. Dr. Erickson and her group will see whether a new artificial intelligence scheme called a "Physics-Informed Neural Network" (PINN) can learn how to efficiently adjust fault model properties to rapidly fit observational data. They will test their PINN first on data from laboratory fault experiments to see how it performs at estimating the already-known fault properties, and then train it until it learns to do this well. Then they will apply the PINN to data from the Pacific Northwest and Costa Rica, where properties and physics of dangerous offshore faults need to be better understood. In addition to their main project, Erickson's team will lead short courses on modern computer programming, data analysis, and AI methods for community college students, using datasets and techniques from this project.Dr. Erickson and her group will apply a deep learning learning technique called the Physics-Informed Neural Network (PINN) to study fault slip, using synthetic and laboratory data, as well as geodetic and seismic data from the Cascadia and Costa Rica subduction zones. Scientific questions concern how heterogeneous fault friction and material properties in subduction zone settings affect fault zone slip, stress, and pore pressure; and how/whether PINNs can be applied to studies of this kind. PINN-based solutions for slip, stress, and pore pressure will be compared with those from traditional computational methods to verify the PINN-based solutions and assess their computational advantages and limitations. The three thrusts of the project are (1) developing the theoretical and computational framework; (2) verifying, validating, and applying methods to (i) analytical solutions and community code verification exercises, (ii) controlled laboratory fault slip experiments, and (iii) natural faults; and (3) training and mentoring students. This project will support two-week mini research experiences for ten community college students, multidisciplinary training at UO and two other universities for several graduate students, and an international collaboration with scientists from Costa Rica.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.

一个故障导致它突然滑倒，释放灾难性地震的是什么，而另一个断层却稳步爬行或逐渐蔓延，或者产生较小，更频繁的地震？这很难评估，因为在地震开始的深度无法直接观察到故障，通常比地面以下5至15英里。我们必须依靠地球表面的仪器进行的间接测量，以及代表故障的计算机模型以及它如何响应地球深处的压力。可以反复调整虚拟断层和周围岩石的属性，直到模型输出数据与地震仪和其他仪器的现实世界观测值紧密匹配的模型。即使科学家使用巧妙的策略，这个过程也很慢而昂贵。埃里克森（Erickson）博士和她的小组将看到一种称为“物理信息神经网络”（PINN）的新人工智能方案（PINN）是否可以学习如何有效调整故障模型属性以快速拟合观察性数据。他们将首先在实验室故障实验的数据上测试PINN，以查看其在估计已知的故障属性方面的性能，然后训练它，直到学会做得很好。然后，他们将把PINN应用于太平洋西北和哥斯达黎加的数据，那里需要更好地理解危险近海断层的属性和物理。除了他们的主要项目外，埃里克森（Erickson）的团队还将使用该项目的数据集和技术来领导现代计算机编程，数据分析和AI方法的简短课程。埃里克森（Erickson）和她的小组将采用一种称为物理信息的神经网络（PINN）的深度学习技术，使用合成和实验室数据以及来自Cascadia和Costa Rica俯冲区域的大地和地震数据来研究断层滑移。科学问题涉及俯冲区设置中的异质断层摩擦和物质特性如何影响断层区的滑动，压力和孔隙压力；以及如何/是否可以将Pinn应用于此类研究。将基于PINN的滑动，压力和孔隙压力的解决方案与传统计算方法的孔隙压力进行比较，以验证基于PINN的解决方案并评估其计算优势和局限性。该项目的三个推力是（1）开发理论和计算框架； （2）验证，验证和将方法应用于（i）分析解决方案和社区代码验证练习，（ii）受控实验室故障滑移实验以及（iii）自然断层； （3）培训和指导学生。该项目将支持为十个社区大学生提供两周的迷你研究经验，在UO和其他两所大学为几位研究生提供多学科培训，并与哥斯达黎加的科学家进行国际合作。该奖项反映了NSF的法定任务，并认为通过基金会的知识素养和更广泛的影响来评估Criteria的评估。