El-Picker: a machine learning-enhanced robust P-phase picker for real-time seismic monitoring

Dazhong Shen, Qi Zhang, Tong Xu, Hengshu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, L. Fang, Enhong Chen, Hui Xiong
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引用次数: 3

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.
El-Picker:用于实时地震监测的增强机器学习鲁棒p相位拾取器
确定地震p相到达时间在地震实时监测中具有重要作用,为应急响应活动提供重要指导。虽然在这方面已经进行了大量的研究,但有效地捕捉隐藏在密集分布和噪声地震波(如破坏性地震余震产生的地震波)中的地震p相的到达时间仍然是一个真正的挑战,因为地震学中大多数常见的现有方法都依赖于艰苦的专家监督。为此,在本文中,我们提出了一种基于集成学习策略EL-Picker的机器学习增强框架,用于自动识别连续和大规模波形上的地震p相到达。更具体地说,EL-Picker由三个模块组成,即触发器、分类器和细化器,并利用集成学习策略集成多个机器学习分类器。对汶川8.0级地震余震的评价表明,EL-Picker不仅能达到最好的识别效果,而且还能识别出120%以上的地震p相到达作为补充资料。同时,实验结果也揭示了不同机器学习模型对不同地震台站采集的波形的适用性,以及人工检测中可能被忽略的地震p相到达规律。这些结果清楚地验证了EL-Picker的有效性、高效性、灵活性和稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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