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

一个断层突然滑动，引发灾难性的地震，而另一个断层却沿着稳步前进，或者产生更小、更频繁的地震，这是怎么回事？这很难评估，因为在地震开始的深度（通常在地下5到15英里）无法直接观察到断层。相反，我们必须依赖于地球表面仪器的间接测量，以及代表断层的计算机模型，以及它如何响应地球深处的压力而滑动。虚拟断层和周围岩石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匹配。这个过程是缓慢和昂贵的，即使科学家使用聪明的策略。埃里克森博士和她的团队将研究一种名为“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故障模型属性，以快速拟合观测数据。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属性方面的表现如何，然后训练它，直到它学会做得很好。然后，他们将PINN应用于太平洋西北部和哥斯达黎加的数据，需要更好地了解危险的近海断层的性质和物理学。除了他们的主要项目外，埃里克森博士的团队还将使用该项目的数据集和技术，为社区大学的学生提供现代计算机编程、数据分析和人工智能方法的短期课程。埃里克森博士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，使用合成和实验室数据，以及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设置中的非均匀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PINN如何/是否可以应用于此类研究。PINN为基础的解决方案的滑移，应力和孔隙压力将与传统的计算方法进行比较，以验证PINN为基础的解决方案，并评估其计算的优势和局限性。该项目的三个重点是（1）开发理论和计算框架;（2）验证，验证和应用方法（i）分析解决方案和社区代码验证练习，（ii）受控实验室断层滑动实验，（iii）自然断层;（3）培训和指导学生。该项目将资助10名社区大学学生进行为期两周的小型研究体验，在UO和其他两所大学为几名研究生进行多学科培训，并与来自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.