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大地震后的余震模式的深度学习
Phoebe M R DeVries1,2, Fernanda Viégas3, Martin Wattenberg3
1Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA. phoebe.devries@uconn.edu.
Nature
|August 31, 2018
概括
一个新的深度学习模型通过分析压力变化准确预测地震余震位置. 这种方法优于传统的应力变化模型, 提供了更好的预测和地震触发机制的洞察力.
科学领域:
- 地震学
- 地质学
- 机器学习
背景情况:
- 余震是由大地震引起的压力变化引发的.
- 经验法则描述了余震的大小和衰变,
- 库伦失效应力变化是余震分布的一个常见但有争议的标准.
研究的目的:
- 开发一种深度学习方法来预测余震地点.
- 在没有故障方向假设的情况下确定基于静态应力的余震预测标准.
- 与现有方法相比,提高余震预测的准确性.
主要方法:
- 运用了一个神经网络, 训练了超过131,000个震后震对.
- 将模型应用于一个独立的测试数据集,
- 将深度学习的预测与经典的库伦失败压力变化进行了比较.
主要成果:
- 深度学习模型的准确度 (AUC为0.849) 高于库伦失效应激变化 (AUC为0.583).
- 学习的模式是物理可解释的,剪切应力和应力张量不变解释了预测方差.
- 确定控制地震触发的关键物理量.
结论:
- 深度学习为预测余震位置提供了更准确的方法.
- 这项研究揭示了地震引发的物理意义上的基于压力的标准.
- 这种方法提高了对地震周期和地震预测能力的理解.
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