Toward earthquake early warning: A convolutional neural network for repaid earthquake magnitude estimation

Fanchun Meng, Tao Ren, Zhenxian Liu, Zhida Zhong
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引用次数: 2

Abstract

Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance. Therefore, in this study, Deep Learning (DL) algorithms are introduced to assist with EEW. For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center (CENC), this paper proposes a DL model (EEWMagNet), which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention. Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude. Moreover, the comparison experiments demonstrate that the epicenter distance information is indispensable, and the normalization has a negative effect on the model to capture accurate amplitude information.

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地震预警:基于卷积神经网络的报复性地震震级估计
地震预警是减少地震灾害的重要手段之一。在当代地震学中,EEW通常被转换为地震震级的快速分类,即需要预警的大震级地震属于积极类别,反之亦然。然而,当前用于幅度快速分类的标准信息信号处理例程是耗时的并且容易受到数据不平衡的影响。因此,在本研究中,引入了深度学习(DL)算法来辅助EEW。针对中国地震台网中心7s的三分量地震波形记录,提出了一种DL模型(EEWMagNet),该模型通过瓶颈密集块和多头注意来实现时空特征的提取。在中国野外数据上的大量实验表明,该模型在震级的快速分类方面表现良好。此外,对比实验表明,震中距离信息是必不可少的,归一化对模型捕捉准确的振幅信息有负面影响。
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