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Rapid characterization and relocation of remotely monitored seismic unrest for improved eruption forecasting using global seismic data

Chanel Deane1, Jeremy Pesicek2, ^^Stephanie Prejean^2^, Paul Earle1, Will Yeck1, David Shelly1

Abstract

Many of the world's volcanoes remain unmonitored seismically at local distances, posing significant challenges for eruption forecasting and monitoring of unrest.  Together, the U.S. Geological Survey's National Earthquake Information Center (NEIC) and Volcano Disaster Assistance Programs (VDAP) are addressing this problem in several ways. We are using regional and teleseismic data from the Global Seismographic Network and other publicly available networks, coupled with advances in detection and analysis methods to:  1) extend earthquake source detection methods, typically focused on high-frequency body waves, to lower frequencies using back-projection methods to improve detection of remote volcanic sources, 2) develop and deploy double-difference relocation methods using both body and surface waves to improve locations and therefore better resolve volcanic processes (e.g., diking), and 3) search global earthquake archives for historical analogs to better interpret seismogenic processes, statistically assess eruptive outcomes, and improve forecasts.  Combined, these efforts are improving our ability to rapidly characterize seismic unrest at remote and unmonitored volcanoes. We present several recent examples where we have applied these methods, including the January 2022 eruption of the Hunga Tonga-Hunga Ha'apai volcano (Tonga), the October 2023 submarine eruption of Sofu Seamount (Izu Islands, Japan), the May 2021 eruption of Mount Nyiragongo (Democratic Republic of the Congo), and the ongoing dike intrusion between Fentale and Dofen volcanoes (Great Rift Valley, Ethiopia).