Japan Geoscience Union Meeting 2024

Presentation information

[E] Oral

S (Solid Earth Sciences ) » S-VC Volcanology

[S-VC25] International Volcanology

Wed. May 29, 2024 1:45 PM - 3:00 PM 201B (International Conference Hall, Makuhari Messe)

convener:Chris Conway(Geological Survey of Japan, AIST), Keiko Matsumoto(Geological Survey of Japan, The National Institute of Advanced Industrial Science and Technology), Taishi Yamada(Sakurajima Volcano Research Center, Disaster Prevention Research Institute, Kyoto University), Masataka Kawaguchi(Earthquake Research Institute, the University of Tokyo), Chairperson:Keiko Matsumoto(Geological Survey of Japan, The National Institute of Advanced Industrial Science and Technology), Taishi Yamada(Sakurajima Volcano Research Center, Disaster Prevention Research Institute, Kyoto University)


1:45 PM - 2:00 PM

[SVC25-01] Using lightning measurements to detect and monitor explosive volcanic eruptions

★Invited Papers

*Sonja A Behnke1, Harald Edens1 (1.Los Alamos National Laboratory)

Keywords:explosive volcanism, lightning, volcano monitoring

Measurements of lightning from explosive volcanic activity can be used both to detect an eruption and infer characteristics of eruption processes. When an explosive eruption occurs, the ensuing electrical activity often follows a predictable pattern. First, a burst of small sparks, often called vent discharges, occurs simultaneous with the onset of an explosion. This burst may last for a few seconds or tens of seconds, depending on the duration of the explosion. Next, lightning discharges start to occur as large-scale charge separation processes begin in the plume, and the burst of vent discharges wanes. The amount of lightning that occurs scales with the overall size of an eruption. This pattern has been observed from explosions of Augustine Volcano (Alaska, USA; 2006), Redoubt Volcano (Alaska, USA; 2009), Eyjafjallajökull (Iceland; 2010), and Sakurajima (Japan; 2015; 2019; 2020). Using very high frequency (VHF) measurements of the radio emissions from the electrical activity during explosive eruptions of Sakurajima we show a method for identifying when an explosion is occurring using machine learning methods. Implementation of this new method would provide new techniques for monitoring and detecting explosive eruptions, and facilitate new research aimed at incorporating lightning observations into volcano monitoring.