CAREER: Magma transport and volcanic seismicity of Hawaii, from the summits to the hotspot

该项目旨在利用微小地震的精确位置来阐明夏威夷活火山下岩浆室和管道的几何形状。这将帮助火山学家了解岩浆是如何从地幔运动到火山的，以及为什么这种运动会引发地震。罗斯博士和他的团队将使用新的人工智能技术来识别过去14年地震仪数据中的微震，这将使记录在案的夏威夷地震的时间至少是以前的十倍。这些地震的位置将勾勒出一些特征，如岩浆室和将这些岩浆室彼此连接起来并连接到火山的管道。对其中一些地震造成的震动的测量也将被用来生成单独的、补充的地下图像(如医学CT扫描)。罗斯博士将参加加州理工学院新设立的“地震研究员”项目，该项目面向洛杉矶地区服务不足的少数民族社区的高中生。夏威夷火山体系的岩浆结构对于理解岩浆从上地幔到个别火山的运输是至关重要的。反过来，这一过程至少是夏威夷地震活动丰富的部分原因。该项目将利用深度学习算法在地震监测方面的最新进展，通过重新处理整个连续波形档案，为夏威夷制作一个大大增强的地震活动目录。该项目的一个关键目标是更好地了解这些运输结构和相关的驾驶过程。作为该项目的一部分，罗斯博士和他的团队将开发一个14年的地震活动目录，以更多地揭示该系统中的岩浆管道及其在地震活动中的作用。这将通过三个相互关联的部分来完成：(I)使用最先进的机器学习反演技术对岩浆结构进行地震层析成像，(Ii)系统地分析整个岛屿的时空地震活动模式和相关的岩浆运输结构，以及(Iii)导出地震源属性，以研究驱动这些事件的物理过程及其与因果运输结构的关系。该奖项反映了NSF的法定使命，并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，被认为值得支持。

This project aims to use precise locations of tiny earthquakes to illuminate the geometry of magma chambers and pipelines beneath Hawaii's active volcanoes. This will help volcanologists understand how magmas move from the Earth's mantle to volcanoes, and why this movement can cause earthquakes. Dr. Ross and his team will use new artificial intelligence techniques to identify microearthquakes in seismometer data from the past 14 years, which will make the list (or 'catalogue') of documented Hawaii earthquakes at least ten times as long as it was before. Locations of these quakes will outline features such as magma chambers and conduits connecting these chambers to each other and to volcanoes. Measurements of shaking caused by some of these earthquakes will also be used to generate a separate, complementary image of the subsurface (like a medical CT scan). Dr. Ross will participate in the newly created “Earthquake Fellows” program at Caltech for high school students from predominantly underserved minority communities in the Los Angeles area. The magmatic architecture of the Hawaiian volcanic system is central to understanding the transport of magma from the upper mantle to the individual volcanoes. This process in turn is at least partly responsible for the abundance of seismicity in Hawaii. This project will leverage recent advances in earthquake monitoring with deep learning algorithms to produce a substantially enhanced seismicity catalog for Hawaii by reprocessing the entire continuous waveform archive. A key goal of the project is to better understand these transport structures and associated driving processes. As part of this project, Dr. Ross and his team will develop a 14-year seismicity catalog to shed more light on the magmatic conduits in this system and their role in generating seismicity. This will be accomplished using three interrelated components: (i) a seismic tomography of the magmatic architecture with a state-of-the-art machine learning inversion technique, (ii) systematic analysis of the spatio-temporal seismicity patterns and the associated magma transport structures across the island, and (iii) derived earthquake source properties to study the physical processes driving these events and their relation to the causative transport structures.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.