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用NESTORE机器学习算法预测希腊随后的强烈地震.

Eleni-Apostolia Anyfadi1,2, Stefania Gentili3, Piero Brondi3

  • 1Section of Geophysics-Geothermics, Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, 15784 Athens, Greece.

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这项研究使用NESTORE机器学习方法预测了希腊地震的强烈余震概率. 该方法准确地识别危险的余震集群,对于减轻地震风险至关重要.

关键词:
希腊人的地震能力.尼斯托尔 (Nestore) 是一个余震是一种余震.集群集群是指一个集群的集群.功能 功能 功能 功能 功能 功能 功能预测 预测 预测 预测机器学习算法机器学习算法培训过程中的培训程序.

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科学领域:

  • 地质物理学 地质物理学
  • 地震学 地震学
  • 机器学习 机器学习

背景情况:

  • 地震余震对城市基础设施构成重大风险,并可能加剧对已经被削弱的结构的损坏.
  • 有效地减轻地震风险需要方法来预测更强的余震事件的概率.

研究的目的:

  • 将NESTORE机器学习方法应用于希腊地震性数据 (1995-2022年) 以预测强烈余震概率.
  • 评估NESTORE算法在对余震集群的分类中的性能及其对减轻地震风险的实用性.

主要方法:

  • 利用NESTORE机器学习算法对1995年至2022年的希腊地震数据进行了分析.
  • 将余震集群分为A型 (更危险,规模差较小) 和B型,基于主震-余震规模差异.
  • 接受了取决于地区的培训,并在一个独立的测试集上评估了绩效.

主要成果:

  • 在电力冲击发生6小时后取得了最佳预测结果.
  • 准确预测了所有集群的92%,包括100%的A型集群和90%以上的B型集群.
  • 在希腊一大片地区展示了精确的集群检测.

结论:

  • 在预测希腊强烈的余震时,NESTORE方法是有效的.
  • 该算法的高精度和快速预测时间使其对地震风险减轻策略具有吸引力.
  • 在希腊的成功应用表明了在类似的地震活跃地区更广泛使用的潜力.