日本地球惑星科学連合2025年大会

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セッション記号 S (固体地球科学) » S-CG 固体地球科学複合領域・一般

[S-CG45] Science of slow-to-fast earthquakes

2025年5月28日(水) 09:00 〜 10:30 国際会議室 (IC) (幕張メッセ国際会議場)

コンビーナ:加藤 愛太郎(東京大学地震研究所)、山口 飛鳥(東京大学大気海洋研究所)、中田 令子(東京大学大学院理学系研究科)、大久保 蔵馬(防災科学技術研究所)、座長:田中 愛幸(東京大学理学系研究科)、Manuel J. Aguilar-Velazquez(Department of Earth and Planetary Science - The University of Tokyo)

10:15 〜 10:30

[SCG45-42] Variability of the Velocity-Acceleration Scaling Relationship in Accelerating Creep

*Chengrui Chang1Hiroyuki Noda2、Qiang Xu3Chao HUANG2、Dongliang Huang4Tetsuo Yamaguchi1 (1.The University of Tokyo、2.Kyoto University、3.Chengdu University of Technology、4.Guangdong Hualu Transportation Technology Co., Ltd.)

キーワード:Landslide, Acceleration, Creep, Friction, Failure-time forecast

Predicting the timing of natural hazards is inherently complex due to the interplay of various physical processes and parameters. The empirical power-law velocity-acceleration scaling relationship, known as the Voight model (Fukuzono, 1985; Voight, 1988), is widely recognized as an effective and reliable tool for forecasting laboratory creep failure and natural events such as landslides and volcanic eruptions.
The exponent in this power-law relationship plays a key role in characterizing precursory accelerating creep behavior of instabilities. Field observations and experiments indicate that this exponent typically ranges from 1 to 2 (e.g., Segalini et al., 2018) but can be significantly lower (e.g., Bozzano et al., 2014) and may evolve over time (e.g., Crosta & Agliardi, 2003; Chang et al., 2024). Rate- and state-dependent friction (RSF) laws, extensively used in fault mechanics, have also been applied to model accelerating landslide creep as a slider block under constant loading. These models predict either a constant α = 2 (Helmstetter et al., 2004; Noda & Chang, 2023) or an evolving α from 1 to 2 (Chang et al., 2024), depending on the frictional properties.
In this presentation, we analyze an extensive dataset of laboratory experiments and field observations to examine the statistical distribution of the exponent. Building on previous theoretical analyses (Noda & Chang, 2023; Chang et al., 2024), we further explore the physical basis for the observed variability through numerical modeling.