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

一个断层导致它突然滑动，引发灾难性的地震，而另一个断层只是稳步移动或产生更小、更频繁的地震，这是怎么一回事？这是很难评估的，因为断层不能直接观察到地震开始的深度，通常是地下5到15英里。取而代之的是，我们必须依靠地球表面的仪器所做的间接测量，以及计算机模型来表示断层以及断层如何随着地球深处的压力而滑动。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结果非常吻合。即使科学家们使用了聪明的策略，这个过程也是缓慢而昂贵的。埃里克森博士和她的团队将观察一种名为“物理信息神经网络”（PINN）的新人工智能方案是否能够学习如何有效地调整故障模型属性，以快速适应观测数据。他们将首先在实验室故障实验的数据上测试他们的PINN，看看它在估计已知故障特性方面的表现如何，然后训练它，直到它能很好地完成这项工作。然后，他们将把PINN应用于太平洋西北地区和哥斯达黎加的数据，在这些地区，危险的近海断层的性质和物理特性需要得到更好的了解。除了他们的主要项目外，Erickson的团队还将为社区大学生提供关于现代计算机编程、数据分析和人工智能方法的短期课程，这些课程将使用该项目的数据集和技术。Erickson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动，使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背景下的非均质断层摩擦和物质性质如何影响断层滑动、应力和孔隙压力；以及pin如何/是否可以应用于这类研究。将基于pass的滑移、应力和孔隙压力解与传统计算方法的解进行比较，以验证基于pass的解，并评估其计算优势和局限性。该项目的三个重点是：(1)发展理论和计算框架；(2)验证、验证和应用方法(i)分析解决方案和社区代码验证练习，（ii）受控的实验室断层滑动实验，以及（iii）自然断层；(3)培训和指导学生。该项目将为10名社区大学生提供为期两周的小型研究体验，为几名研究生提供在UO和另外两所大学的多学科培训，并与哥斯达黎加科学家进行国际合作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.