Earthquake events classification using convolutional recurrent neural network

IF 0.2 Q4 ACOUSTICS
Bonhwa Ku, Gwantae Kim, Su Jang, Hanseok Ko
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引用次数: 0

Abstract

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multiinput single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.
基于卷积递归神经网络的地震事件分类
本文提出了一种能够同时反映地震波形静态和动态特征的卷积递归神经网络(CRNN)结构,用于各种地震事件的分类。处理各种地震事件,不仅包括微地震和人工地震,还包括宏观地震,既需要有效的特征提取,也需要能够在噪声环境下识别地震波形的分类器。首先,通过基于注意的卷积层提取地震波形的静态特征。然后,将提取的特征图依次作为输入注入到多输入单输出的长短期记忆(LSTM)网络结构中,提取地震事件分类的动态特征。随后,我们通过两个完全连接的层和softmax函数进行地震事件分类。国内外具有代表性的地震数据库实验结果表明,该模型为各种地震事件分类提供了一种有效的结构。
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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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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