使用随机森林分析检测大气度异常及其对地震预测潜力的探测
Mayu Tsuchiya1, Hiroyuki Nagahama2, Jun Muto2
1Department of Earth Science, Graduate School of Science, Tohoku University, 6-3 Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8578, Japan. mayu.tsuchiya.r3@dc.tohoku.ac.jp.
Scientific reports
|May 31, 2024
概括
(222Rn) 的波动可能会在地震之前发生. 随机森林的分析客观地确定了大规模地震事件之前大气中显著的异常,表明其对地震预测的潜力.
科学领域:
- 地质物理学 地质物理学
- 环境科学 环境科学
- 地震学 地震学
背景情况:
- 地震预测研究研究前体异常,包括放射性 (222Rn) 气体的波动.
- 土壤,水和大气中的度变化与地运动有关.
- 之前分析异常的统计方法存在局限性.
研究的目的:
- 客观地分析大气度数据,以发现地震前的异常.
- 评估随机森林分析在检测地震前体方面的有效性.
- 改进现有的定量分析方法.
主要方法:
- 通过随机森林分析,将观察到的大气度与预测的年度模式进行了比较.
- 利用了神户制药大学 (在1995年神户地震前) 的大气数据.
- 福岛医科大学 (在2011年东北地震前) 的最新数据使用了电离.
主要成果:
- 随机森林分析发现了在两次地震之前大气度的统计学上显著的异常.
- 预测和观察到的值之间的偏差超过了地震事件发生前标准偏差的三倍.
- 该方法使用定义的值提供了更客观的异常确定.
结论:
- 随机森林分析表明,有可能识别大气异常作为地震前体.
- 这种客观的方法可以提高基于的地震事件检测的可靠性.
- 需要进一步的研究来验证和完善这种预测方法.
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