Japan Geoscience Union Meeting 2025

Presentation information

[E] Poster

M (Multidisciplinary and Interdisciplinary) » M-IS Intersection

[M-IS09] Interdisciplinary studies on pre-earthquake processes

Sun. May 25, 2025 5:15 PM - 7:15 PM Poster Hall (Exhibition Hall 7&8, Makuhari Messe)

convener:Katsumi Hattori(Department of Earth Sciences, Graduate School of Science, Chiba University), Jann-Yenq LIU(Center for Astronautical Physics and Engineering, National Central University, Taiwan), Dimitar Ouzounov(Chapman University), Qinghua Huang(Peking University)

5:15 PM - 7:15 PM

[MIS09-P05] A novel LF/VLF signal discrimination using random forest and interferometer approaches; identification of lightning discharge and possible seismo-EM signals

Yuichiro Ota1, *Katsumi Hattori2,3,4, Kenshin Miura5, Chie Yoshino2, Noriyuki Imazumi6 (1.Graduate School of Science and Engineering, Chiba University, 2.Graduate School of Science, Chiba University, 3.Center for Environmental Remote Sensing, Chiba University, 4.Research Institute of Disaster Medicine, Chiba University, 5.Faculty of Science, Chiba University, 6.The Institution of Professional Engineers, Chiba Branch, Japan)

Keywords:LF/VLF signal discrimination, random forest, interferometer, lightning discharge processes, seismo-electctromagnetic signals

Electromagnetic phenomena preceding large earthquakes have been reported in various frequency bands. As an example, Yamada and Oike 1996 reported an increase in the number of pulses in the LF band before the 1995 M7.2 Hyogo-ken Nanbu earthquake. On the other hand, Izutsu 2007 pointed out that some of the increased pulses were likely due to lightning activity. Therefore, for the identification of VLF/LF signals related to earthquakes, the existence of electromagnetic emissions unrelated to earthquakes is a problem, especially the discrimination of electromagnetic emissions from lightning activity. Therefore, we have conducted the following two approaches to construct a system that can properly identify and remove electromagnetic emissions caused by lightning activity, which can be noise. The first approach is to use machine learning to identify and remove known lightning pulses from data obtained at a single observation point. In this study, a random forest, a type of machine learning, was constructed and applied to observed data. The second is to develop a VLF/LF broadband interferometer with antenna systems installed at several stations to construct a system to estimate electromagnetic radiation sources. In this study, observation tests were conducted at three stations: Chiba University, Asahi, and Miho. We compared estimated location of lightning by LIDEN with estimated location of lightning by our VLF/LF broadband interferometer. The details on the results of conducting these two approaches will be reported in the presentation.