通过将机器学习算法纳入近乎实时的操作后处理的改进
Gabrielle Tepp1, Ellen Yu2, Aparna Bhaskaran2
1Seismological Laboratory, Caltech, 1200 E. California Blvd 252-21, Pasadena, CA, 91125, USA. gtepp@caltech.edu.
Scientific reports
|August 7, 2025
概括
像PhaseNet和GaMMA这样的机器学习算法增强了地震数据分析. 这些工具提高了地震检测和震中精度,减少了分析师在地震监测中的工作量.
科学领域:
- 地球科学 地球科学 地球科学
- 地质物理学 地质物理学
- 地震学 地震学
背景情况:
- 实时地震监测依赖于自动数据分析来识别事件.
- 小地震事件的后处理 (M < 3) 在分析师审查之前细化数据.
- 机器学习 (ML) 算法越来越适用于地震相选.
研究的目的:
- 实施和评估用于地震事件后处理的ML算法.
- 提高地震事件检测和定位的准确性和效率.
- 为了减少地震数据分析师的工作量.
主要方法:
- 南加州地震网络集成了深度学习的PhaseNet,以改进阶段选择.
- 使用PhaseNet和GaMMA ML算法开发了一个自动后处理管道 (ST-Proc).
- 对于选择数量和准确性,PhaseNet与传统的STA/LTA选择器进行了比较.
主要成果:
- 与STA/LTA相比,PhaseNet的精度提高了2-3倍,特别是S阶段,精度提高了.
- 由ML驱动的后处理导致了更高的震中精度.
- 在ST-Proc管道中,从子网络触发器实现了65-70%的事件检测,错误事件率为5%.
- 用GaMMA确定的震中显示出很好的准确性,距离最终位置几公里之内.
结论:
- 机器学习算法显著改善了地震事件的检测和表征.
- 使用PhaseNet和GaMMA的自动化管道简化了地震数据处理.
- 这些进步减少了分析师的工作量,并提高了整体监控效率.
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