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.

一个断层为何会突然滑动，引发灾难性地震，而另一个断层则稳定地蠕动或产生更小、更频繁的地震？这很难评估，因为在地震发生的深度（通常是地下 5 至 15 英里）无法直接观察到断层。相反，我们必须依靠地球表面仪器进行的间接测量，以及代表断层及其如何响应地球深处压力而滑动的计算机模型。虚拟断层和围岩的属性可以反复调整，直到模型输出的数据与地震仪和其他仪器的真实观测数据紧密匹配。即使科学家使用巧妙的策略，这个过程也是缓慢且昂贵的。埃里克森博士和她的团队将看看一种名为“物理信息神经网络”（PINN）的新人工智能方案是否可以学习如何有效地调整断层模型属性以快速拟合观测数据。他们将首先使用实验室故障实验的数据测试 PINN，看看它在估计已知故障属性方面的表现如何，然后对其进行训练，直到它学会很好地做到这一点。然后，他们将 PINN 应用到来自太平洋西北地区和哥斯达黎加的数据，需要更好地了解这些地区危险的近海断层的特性和物理性质。除了他们的主要项目之外，埃里克森的团队还将利用该项目的数据集和技术，为社区学院的学生开设有关现代计算机编程、数据分析和人工智能方法的短期课程。埃里克森和她的团队将应用一种名为物理信息神经网络（PINN）的深度学习技术来研究断层滑移，利用合成数据和实验室数据，以及来自卡斯卡迪亚和哥斯达黎加俯冲带的大地测量和地震数据。科学问题涉及俯冲带环境中的异质断层摩擦和材料特性如何影响断层带滑动、应力和孔隙压力；以及如何/是否可以将 PINN 应用于此类研究。基于 PINN 的滑移、应力和孔隙压力解决方案将与传统计算方法的解决方案进行比较，以验证基于 PINN 的解决方案并评估其计算优势和局限性。该项目的三个重点是（1）开发理论和计算框架； (2) 验证、确认方法并将其应用于 (i) 分析解决方案和社区代码验证练习，(ii) 受控实验室断层滑移实验，以及 (iii) 自然断层； (3) 培训和指导学生。该项目将为 10 名社区学院学生提供为期两周的迷你研究体验，为数名研究生提供在俄勒冈大学和另外两所大学的多学科培训，以及与哥斯达黎加科学家的国际合作。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。