EMBRACE-EAR-SEED: Top-Down or Bottom-Up: Can Shear Wave Splitting Analysis Illuminate the Dominant Driver of Short-Term Magmatic Flow?

This project investigates the magma system beneath Kīlauea Volcano, Hawaii using seismic anisotropy—a phenomenon where seismic waves travel at different speeds depending on the properties of the materials they pass through. The research focuses on understanding how changes in subsurface magma influence volcanic activity, with the goal of improving methods for eruption forecasting.

Shear wave splitting (SWS) analysis, which examines how seismic waves split as they travel through different materials, can provide information about subsurface conditions. This study will address fundamental questions about magma transport and the interplay between deep and shallow magma systems, helping to enhance the predictive capabilities for volcanic eruptions.

The findings have the potential to enhance eruption forecasting methods, benefiting not only volcanology but also communities and infrastructure near active volcanoes. Conducted at a two-year college, this project also offers students hands-on experience in advanced research techniques and promotes skill development in data analysis and geoscience methodologies.

The project aspires to engage the broader community through potential outreach activities, such as presentations and public talks, to foster greater understanding of volcanic processes and hazards.

This study utilizes SWS analysis of seismic data collected during the 2018 Kīlauea eruption. SWS occurs when seismic waves split into fast and slow components as they pass through anisotropic materials and can provide insights into stress fields, magma flow, and microfracture alignment within a volcanic system. The project will analyze deep earthquakes (>15 km) near Pāhala, a potential magma supply zone, and shallow earthquakes (<5 km) at Kīlauea’s summit and Lower East Rift Zone.

By correlating these findings with other monitoring data—such as deformation and gas emissions—the study aims to characterize how changes in deep magma systems influence surface volcanic activity. This work will provide valuable insights into the dynamics of magma transport and improve predictive models for volcanic eruptions.

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.