机器学习可以预测米级实验室地震
Reiju Norisugi1, Yoshihiro Kaneko2, Bertrand Rouet-Leduc3
1Department of Geophysics, Kyoto University, Kyoto, Japan. norisugi.reiju.77e@st.kyoto-u.ac.jp.
Nature communications
|October 31, 2025
概括
机器学习通过分析声学排放,准确地预测米级实验室地震. 这种方法通过跟踪断层应力演变,为预测自然地震提供了洞察力.
科学领域:
- 地质物理学 地质物理学
- 地震科学 地震科学 地震科学
- 机器学习应用 机器学习应用
背景情况:
- 在岩石摩擦实验中,对机器学习 (ML) 越来越感兴趣,用于预测实验室地震 (剪切滑动故障).
- 由于时间尺度的巨大变化,对更大规模的实验室地震和自然地震的ML适用性存在不确定性.
研究的目的:
- 将先进的ML方法应用于仪表尺度实验室地震数据.
- 评估ML在预测大规模地震事件的失效时间方面的能力.
主要方法:
- 在米尺度实验室地震数据上使用先进的ML方法.
- 雇佣了ML模型培训活动目录的网络代表.
- 将ML预测与剪切故障的动态模型进行比较.
主要成果:
- 准确地预测了计量尺度主震动的故障时间,从前几秒到几毫秒.
- 证明了ML在与自然地震相关的时间尺度 (几十年到几周) 上预测事件的能力.
- 识别了跟踪在爬行断层上的剪切应力演变,作为ML预测的关键.
结论:
- 通过分析声辐射事件,ML可以有效地预测实验室地震.
- 研究结果表明,ML可以间接追踪断层应力,这对于地震预测至关重要.
- 为自然地震的短期预测提供了关键的见解.
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