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

California Institute Of Technology, Pasadena CA

Investigators

Abstract

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 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.