PREEVENTS Track 2: Cascadia Tsunami Warning with Data Assimilation and Optimal Sensor Distribution

卡斯卡迪亚俯冲带对美国太平洋西北部人口稠密的沿海地区构成了重大的地震和海啸威胁。该项目的目标是通过几方面的研究提高本地区的海啸预警能力。第一种是采用资料同化的方法，将到达海洋观测点的海啸波观测资料用于预测海岸海啸。二是检验船舶位置数据在海啸预警中的应用。海啸波在开阔的海洋中很小，但可以引起船只高度和航向的明显变化。这些船舶数据可以为改进海啸预报提供有用的信息。该项目的最后一个方面是确定美国太平洋西北部近海海啸预警海底观测点的最佳间隔。安装海底观测点是昂贵的，本研究将使用数学方法来确定考虑不同场景的海底传感器的最佳位置。该项目的预期结果将有助于设计未来的Cascadia近海观测网络和其他俯冲带，以提供地震和海啸预警能力。该项目的动机是提高海啸预警的速度和准确性，并设计最佳的海啸预警探测阵列。本项目将探讨数据同化和最佳传感器分布在卡斯卡迪亚海啸预警中的应用。多种基于海洋的观测数据将被利用，包括来自卡斯卡迪亚倡议实验的海底压力记录、美国国家海洋和大气管理局深海海啸评估和报告（DART）海底压力数据、沿海潮汐计数据和来自船载全球导航卫星系统的海面高度数据。这项工作的重点将放在：(1)发展海啸预警的数据同化方法，包括快速地震大地测量源解决方案、船舶高度数据和海啸波段格林？(2)检查gnss衍生的船舶高度数据在当地海啸预警中的效用，以及(3)确定离岸卡斯卡迪亚俯冲带海啸传感器的最佳分布。数据同化是一种在天气预报中使用多年的方法，它利用实时观测来进行预报。资料同化是一项迅速发展的研究，只是最近才应用于海啸研究。除了利用数据同化来更好地预测海啸外，本工作还将探索传感器的最佳间距，为沿海地区提供及时准确的海啸预警。而不是部署许多站，这可能是昂贵的，可以通过数值模拟探索一个最优的观测网设计。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。

The Cascadia Subduction Zone poses a significant earthquake and tsunami hazard to the densely populated coastal regions of the US Pacific Northwest. The goals of this project are to improve the capability for tsunami warning in this region by several lines of research. The first is to use the method of data assimilation, which takes tsunami wave observations as they arrive at ocean observing points and uses them to forecast the tsunami at the coast. The second is to test the use of ship position data in tsunami warnings. Tsunami waves are small in the open ocean but can cause detectable changes in ship height and heading. These ship data could provide useful information to improve tsunami forecasts. The last aspect of the project is to determine the best spacing of seafloor observing sites for tsunami warnings offshore the US Pacific Northwest. Installing seafloor observing sites is costly, and this study will use mathematical methods to determine the best places for seafloor sensors considering different scenarios. Anticipated results from this project will help inform efforts to design future observation networks offshore Cascadia and other subduction zones for providing earthquake and tsunami early warning capability.The motivation for this project is to improve the speed and accuracy of tsunami warnings and to best design detection arrays for tsunami warning. This project will explore the use of data assimilation and optimal sensor distribution for Cascadia tsunami warning. Multiple ocean-based observational data will be utilized including seafloor pressure recordings from the Cascadia Initiative experiment, National Oceanic and Atmospheric Administration Deep Ocean Assessment and Reporting of Tsunamis (DART) seafloor pressure data, coastal tide gauge data, and sea surface height data from ship-borne Global Navigation Satellite Systems. The emphasis of this work will be on (1) development of data assimilation methods for tsunami warnings by including rapid seismogeodetic source solutions, ship height data, and tsunami-band Green?s functions in addition to dense arrays of seafloor pressure data, (2) examination of the utility of GNSS-derived ship height data for local tsunami warning, and (3) determination of optimal distribution of tsunami sensors in the offshore Cascadia Subduction Zone. Data assimilation is a method that has been used in weather forecasting for years, and uses real-time observations to develop a forecast. Data assimilation is a rapidly developing line of research and has only recently been applied to tsunami studies. In addition to utilizing data assimilation to better forecast tsunamis, this work will explore the optimal spacing of sensors to provide timely and accurate tsunami warnings to the coastal regions. Instead of deploying many stations, which may be prohibitively expensive, an optimal observation network design can be explored through numerical simulations.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.