This project seeks to improve the understanding of earthquakes and tsunamis in subduction zones. Special tectonic signals that can be measured at the seafloor may represent the release of tectonic stress in subduction zones. If so, measurements from pressure sensors on the seafloor could be used to estimate earthquake and tsunami risks. However, noise from ocean processes makes it difficult to detect this signal accurately. This project will take advantage of recent advances in a computational technique, machine learning, to develop a better detector of this signal.. This project will support early career scientists and people from underrepresented groups (Latino and Female) in STEM fields. It will also support a graduate student and several undergraduates. This project will develop teaching modules of machine learning at the graduate, undergraduate, high school, and middle school levels. This project will publish code in the public domain and share the teaching modules within the community immediately after the project finishes.

Shallow slow slip events provide a mechanism for strain release at the shallow part of subduction zones, which is important for tsunami hazard assessment. For most subduction zones, the trench is far from the coast and it is unclear whether shallow slow slip events exist. Even in places where these events were detected, key quantities such as the duration and magnitude were not well constrained. As a result, the locking state of shallow subduction zones and the mechanism of shallow slow slip events is still unclear. To answer these questions, this project will take advantage of recent advancement in machine learning and the accumulation of seafloor pressure datasets to improve our ability to detect shallow slow slip events in subduction zones. Preliminary analyses of seafloor pressure data from New Zealand have demonstrated that machine learning can successfully identify known slow slip events and further reduce ocean noise in seafloor pressure data. Using available data from several subduction zones, this project will further improve the machine-learning detector to estimate the duration, amplitude, and timing of shallow slow slip events. This project will also develop an improved way to reduce ocean noise in seafloor pressure data by using machine learning to capture the complex relationship of measurable quantities in the ocean. Collectively, this project will provide better tools to measure shallow slow slip events and assess the locking state of shallow subduction zones.

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.

National Science Foundation (NSF)
Division of Ocean Sciences (OCE)
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Deborah K. Smith
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University of Rhode Island
United States
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