通过深度学习,探测地震周期期间断层性质的演变
Laura Laurenti1, Gabriele Paoletti2, Elisa Tinti2
1Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy. laura.laurenti@uniroma1.it.
Nature communications
|November 20, 2024
概括
深度学习模型准确地分类来自诺西亚地震的地震波,区分前震和余震. 这种方法显示了跟踪断层区域属性的潜力,并改进了地震预警系统.
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:
- 2016年M6.5诺西亚地震为研究地震波传播提供了一个独特的数据集.
- 了解地震前后的变化对于地震危险评估至关重要.
- 深度学习 (DL) 为分析复杂的地震数据提供了新的方法.
研究的目的:
- 将深度学习应用于地震数据,以区分地震阶段 (前震,余震,时间到故障).
- 调查DL模型在地震周期中描述断层区域属性的能力.
- 评估DL在加强地震预警和地震危险分析方面的潜力.
主要方法:
- 使用来自诺西亚地震的原始地震记录作为七层卷积神经网络 (CNN) 的输入.
- 培训和测试基于时间到故障 (TTF) 的二进制和N类DL模型.
- 评估模型性能使用原始与过的地震数据和控制测试与错误的电源冲击时间或遥远的地震波.
主要成果:
- DL模型在与前震,余震和TTF相关的地震图分类方面取得了超过90%的准确性.
- DL模型成功地区分了震前和震后的地震波,与裂密度的理论变化保持一致.
- 使用过数据 (<10 Hz) 的性能降低表明DL模型捕捉了弹性波衰减的微妙变化.
结论:
- 深度学习模型可以有效地区分地震波类型,并跟踪断层区域属性演变.
- 这些发现表明DL可以作为整个地震周期中断裂特性的代理.
- 这些DL模型的普遍应用可以显著改善地震预警和地震危险分析.
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