[S-VC30] Mitigation of volcanic disasters - basic and applied researches

[SVC30-11] Development of an Optimized Observation System for Rockfalls on Mt. Fuji

*Taishi Sakurada¹, Tomohiro Kanzaki¹, Yasuhiro Ishimine², Hideaki Miyamoto¹ (1.The University of Tokyo, 2.Mount Fuji Research Institute, Yamanashi Prefectural Government)

Keywords:rockfall, Mt.Fuji, Hoei Crater

Rockfalls are commonly-occurring, damaging natural disaster processes often observed on mountain slopes. They are also reported on several extraterrestrial bodies, including the Moon (e.g. Bickel et al., 2020), Mars (e.g. Kumar et al., 2019), and asteroids (e.g. Jawin et al., 2020), where some crucial topographic features are explained only by their repetitious occurrences. A rockfall usually occurs when a part of outcrops or sediments detaches due to physical and biological weathering processes (e.g. Matsuoka, 1991). Then, the rock moves at high speed down a steep mountain slope, repeating collisions and crushing. Even though a rockfall is not an unusual geological process on Earth, its nature is not fully understood because of its short duration and contingency. While many rockfalls have been studied after their occurrences and their behaviors are theoretically or numerically studied (e.g. Li & Lan, 2015), real-time, precise, and detailed observations of rockfalls are still challenging.
We are interested in the Hōei Crater of Mount Fuji because of its geological similarities to asteroids and the Moon and its high frequency of reported rockfalls, which may provide the best opportunities to observe rockfalls in real-time. We are developing an autonomous observation system with sufficient weather resistance, a stable power source, and the capability to describe the phenomena at high resolution: The camera is protected by a lightweight waterproof box, and the cables are protected by waterproof connectors; We use solar cells connected to a battery with a sufficiently large capacity to secure a stable power supply; We develop an autonomous mechanism which cuts off the power when the system is not in use; We implemented a machine-learning-based motion detection algorithm to reduce the total amount of data. This newly-developed observation system has been tested for a preliminary evaluation both in the laboratory and in the field.

References
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