用深度学习进行现场地震预警的地面加速峰值预测
Yanqiong Liu1, Qingxu Zhao2, Yanwei Wang3
1China Earthquake Network Center, Beijing, China.
Scientific reports
|March 6, 2024
概括
一个新的深度学习模型 (DLPGA) 准确地使用初始地震波数据预测顶峰地面加速 (PGA). 这种先进的方法通过提供比传统方法更快,更精确的损害评估来改善地震预警系统 (EEW).
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 人工智能的人工智能
背景情况:
- 准确预测地面加速峰值 (PGA) 对于地震损害评估在现场地震预警 (EEW) 中至关重要.
- 目前的EEW方法依赖于人类经验选择的P波特征,限制了预测的准确性和及时性.
- 功能选择的局限性阻碍了传统EEW系统的有效性.
研究的目的:
- 为快速和准确的PGA预测开发一个端到端的深度学习模型.
- 克服EEW中人类定义的特征参数的局限性.
- 提高PGA预测的准确性和及时性,以改善地震损害评估.
主要方法:
- 使用卷积神经网络 (CNN) 开发了一个端到端的深度学习模型来预测PGA (DLPGA).
- DLPGA模型采用来自单个站的垂直初始3-6秒地震波作为输入.
- 功能通过多层CNN自动提取,以便快速预测PGA.
主要成果:
- 与峰值位移 (Pd) 方法相比,DLPGA显示PGA预测的相关系数增加了12-23%
- 使用最初的3-6秒地震波,误差的标准偏差下降了22-25%,误差平均值下降了6.92-19.66%.
- 与PD相比,DLPGA表现优越,甚至在3s的数据中更准确地预测PGA,而在6s的数据中比PD更准确,并且改善了35-150%的地面运动破坏性识别.
结论:
- 与依赖于人工定义的特征参数的方法相比,DLPGA提供了显著的准确性和及时性优势.
- 该模型自动提取特征的能力提高了PGA对现场EEW的预测.
- DLPGA可以显著提高EEW系统在评估地面运动破坏性的有效性.
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