[论文解读] Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring - The Winning Solution of the 2017 International Aftershock Detection Contest.

Dazhong Shen, Qi Zhang+8 more|arXiv (Cornell University)|Nov 21, 2019

Seismology and Earthquake Studies参考文献 27被引用 3

一句话总结

本文提出EL-Picker，一种结合机器学习的集成框架，用于在噪声大、余震密集的环境中实时检测地震P波初至。通过整合触发器（Trigger）、分类器（Classifier）和精炼器（Refiner）模块并采用集成学习，EL-Picker实现了卓越性能——相比现有方法识别出多出120%的P波初至事件，展现出在各类地震台站中均具备高精度、高效率和强鲁棒性。

ABSTRACT

Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. While considerable research has been conducted on this topic, efficiently capturing the arrival times of seismic P-phases hidden within intensively distributed and noisy seismic waves, such as those generated by the aftershocks of destructive earthquakes, remains a real challenge since most common existing methods in seismology rely on laborious expert supervision. To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms. More specifically, EL-Picker consists of three modules, namely, Trigger, Classifier, and Refiner, and an ensemble learning strategy is exploited to integrate several machine learning classifiers. An evaluation of the aftershocks following the MS 8.0 Wenchuan earthquake demonstrates that EL-Picker can not only achieve the best identification performance but also identify 120% more seismic P-phase arrivals as complementary data. Meanwhile, experimental results also reveal both the applicability of different machine learning models for waveforms collected from different seismic stations and the regularities of seismic P-phase arrivals that might be neglected during manual inspection. These findings clearly validate the effectiveness, efficiency, flexibility and stability of EL-Picker. Note that this paper is the English version of our work published in Science China Information Science (http://engine.scichina.com/doi/10.1360/SSI-2020-0214).

研究动机与目标

• 解决在高度噪声、波形密集的余震环境中检测P波初至的挑战。
• 减少地震监测中对人工密集型专家监督的依赖。
• 开发一种可扩展的自动化系统，能够处理持续的、大规模的地震数据流。
• 提升实时地震监测应用中的检测准确率与完整性。
• 识别出人工检查可能遗漏的微弱或被忽视的P波初至模式。

提出的方法

• 采用集成学习策略，结合多个机器学习分类器以提升鲁棒性与准确性。
• 设计三模块流水线：触发器（初始事件检测）、分类器（P波初至到达时间估计）和精炼器（后处理以提升精度）。
• 利用来自多个地震台站的波形数据训练多种机器学习模型，确保对台站特异性特征的适应能力。
• 应用特征工程与时间建模技术，从连续地震波形中提取具有区分性的模式。
• 通过集成平均或投票机制整合多个分类器的预测结果，以降低单个模型的偏差。
• 优化框架以实现实时推理，确保在流式地震数据上实现低延迟处理。

实验结果

研究问题

• RQ1 集成机器学习框架是否能在噪声较大的余震波形中优于传统方法检测P波初至？
• RQ2 自动化检测系统在多大程度上能够识别出超出人工检测极限的P波初至事件？
• RQ3 不同机器学习模型在具有不同噪声特征的多样化地震台站波形上表现如何？
• RQ4 自动化系统能够检测到哪些在人工检查中被忽视的P波初至隐藏规律？
• RQ5 所提出的框架是否能在实时监测中保持在不同地震条件下的高稳定性与高准确性？

主要发现

• EL-Picker在2017年国际余震检测竞赛中所有评估方法中表现最佳。
• 该框架识别出的地震P波初至事件比现有方法多出120%，显著提升了检测完整性。
• 不同机器学习模型在来自各类地震台站的波形上均表现出强适应性，表明其具有良好的泛化能力。
• 系统揭示了以往人工检查中被忽略的一致性P波初至到达模式，凸显了地震数据中隐藏的规律性。
• EL-Picker在实时处理连续、大规模地震波形时表现出高稳定性、高效率和高灵活性。
• 集成学习策略有效降低了模型方差，提升了在多样化地震条件下的检测可靠性。