Synergistic study of deformation signals and volcano-tectonic earthquakes at Mauna Loa Volcano, HI

Pennsylvania State Univ University Park, University Park PA

Investigators

Abstract

Mauna Loa Volcano in Hawai’i is one of the largest active volcanoes on Earth. The volcano showed clear signs of unrest from 2014, leading up to its November 2022 eruption. The long duration of activity gives scientists a chance to study the causes of earthquakes and ground movement in a complex volcano that has multiple magma chambers and rift zones. Researchers will use satellite data (InSAR) to detect ground changes linked to volcanic unrest. This research will use studies of earthquake patterns and other seismic data to help scientists understand the causes of volcano deformation. In this work, scientists will learn about the warning signs before major eruptions on Mauna Loa’s flanks that can endanger many people. Researchers will also mentor young scientists and produce a course activity about volcano monitoring. Since 2014, Mauna Loa Volcano has been closely monitored using dense Interferometric Synthetic Aperture Radar (InSAR) geodetic and seismic datasets, leading up to its November 2022 eruption. Researchers will use this rich dataset to study ground deformation patterns and identify short-term deformation events. They will develop and apply an advanced deep learning method—based on a convolutional neural network. This approach will help separate real volcanic deformation signals from noise caused by the atmosphere in the InSAR data. The method will account for random and layered atmospheric effects and will be tested against GNSS ground-based data to ensure accuracy. Next, the team will link the deformation signals to magma movement beneath the surface, which eventually caused the eruption. This will be done using a complementary seismic analysis of earthquake locations, migration patterns, and fault-plane solutions. Special attention will be given to the interactions between shallow magma reservoirs and fault systems beneath the summit caldera. A Coulomb stress change analysis will help test the hypothesis that earthquake activity near the volcano is influenced by how the shallow magma system is inflating or deflating. The project will provide research opportunities and support for a postdoctoral scholar at Penn State, as well as an undergraduate honor student, who will contribute to InSAR data processing and the creation of a new catalog of volcano-tectonic earthquakes. This work will be done in collaboration with the USGS Hawaiian Volcano Observatory (HVO), including on-site training and seismic analysis guidance. Findings from this project will help HVO improve its volcano monitoring and eruption forecasting capabilities. More broadly, the results have important implications for understanding and forecasting eruptions at basaltic volcanoes with complex rift systems around the world. One of the project’s key outcomes will be the creation of a new deep learning training dataset and denoising approach, with the goal of improving the accuracy of InSAR measurements worldwide. These tools, including algorithm codes, will be made publicly available through Penn State’s ScholarSphere repository. Additionally, a new online learning activity will be developed for GEOSC 30: Volcanoes!, a general education course at Penn State. The activity will focus on analyzing volcanic unrest signals, especially GNSS data before, during, and after the 2022 Mauna Loa eruption. It will also include hazard assessment through lava flow mapping using satellite imagery. 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.