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方岩浆室和管道的几何形状。这将帮助火山学家了解岩浆如何从地幔移动到火山，以及为什么这种运动会引起地震。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地震，这将使记录在案的夏威夷地震列表（或“目录”）至少比以前长十倍。这些地震的位置将勾勒出诸如岩浆室和将这些室相互连接以及与火山连接的管道等特征。对其中一些地震引起的震动的测量也将用于生成地下的单独的补充图像（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”计划，该计划面向来自洛杉矶地区服务水平低下的少数族裔社区的高中生。夏威夷火山系统的岩浆结构对于理解岩浆从上地幔到各个火山的输送至关重要。这一过程至少是夏威夷地震活动频繁的部分原因。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，生成大幅增强的夏威夷地震活动目录。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 年地震活动目录，以更多地了解该系统中的岩浆通道及其在产生地震活动中的作用。这将通过三个相互关联的组成部分来完成：(i) 采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) 对全岛时空地震活动模式和相关岩浆输送结构进行系统分析，以及 (iii) 导出地震源特性，以研究驱动这些事件的物理过程及其与致因输送结构的关系。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

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.