基于卷积神经网络的速度大脉冲识别方法研究

牛志辉; 陈波; 卜春尧

doi:10.11899/zzfy20210307

基于卷积神经网络的速度大脉冲识别方法研究

doi: 10.11899/zzfy20210307

中国地震局地球物理研究所，北京 100081

基金项目: 国家重点研发计划（2019YFC1509402）；中国地震局地球物理研究所基本科研业务费专项（DQJB19A0132）

详细信息

作者简介:
牛志辉，男，生于1992年。硕士研究生。主要从事人工智能算法的速度脉冲型地震动研究。E-mail：niuzhihui18@mails.ucas.ac.cn

通讯作者:
陈波，男，生于1987年。副研究员。主要从事地震动特性研究。E-mail：chenbo@cea-igp.ac.cn

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出版历程
- 收稿日期: 2021-03-30
- 刊出日期: 2021-09-30

Identification of Pulse-like Strong Ground Motions Based on Convolution Neural Network

Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

摘要

摘要: 含速度大脉冲的强地震动具有复杂的特性，人工提取速度大脉冲特征的方法较繁琐，故利用卷积神经网络（CNN）在图像特征自动提取方面的优势，提出基于卷积神经网络图像识别的速度大脉冲识别方法。基于美国太平洋地震工程研究中心NGA-West1数据库提供的强地震动记录，筛选出6 000条非脉冲记录和91条含有速度大脉冲的强地震动记录。采用在原始记录中加入高斯噪声和过采样的方法，使2类记录样本数量达到均衡。利用本文建立的卷积神经网络模型对2类记录速度时程图进行特征自动提取和分类识别，结果显示测试集准确率为99%，表明本文卷积神经网络模型能够自动提取速度大脉冲特征，进而复现已有结果。将本文方法与传统方法进行了对比，结果表明，对含有多个速度脉冲的强地震动记录的识别，本文方法优于传统方法，具有较高的可靠性、鲁棒性、灵活性。
- 强地震动 /
- 速度脉冲 /
- 卷积神经网络（CNN） /
- 图像识别
Abstract: Aiming at the tedious problem of strong ground motion containing big velocity pulse, which has complex characteristics and requires manual feature extraction. Taking advantage of the Convolutional Neural Network (CNN) in the automatic extraction of image features, we propose a big velocity pulse recognition method based on CNN image recognition. Firstly, based on the strong motion records provided by the NGA-West1 database of the Pacific Earthquake Engineering Research Center of the United States, 6000 non-pulse records and 91 strong ground motion records containing big velocity pulse were screened out. Secondly, using the method of adding Gaussian noise and oversampling to the original record, the number of samples of the two types is balanced, and the number of samples of each type is 6000. Then, the CNN model established in this paper is used to automatically extract features and classify and recognize the two types of recording speed time history graphs, the results show that the accuracy of the test set is 99%. Finally, the method in this paper is compared with the traditional method, and it is better than the traditional method in identifying multiple velocity pulses. It shows that the method in this paper can automatically extract high-speed pulse information for identification, and has high effectiveness, robustness and flexibility.
- Strong ground motion /
- Velocity pulse /
- Convolution neural network /
- Image recognition

HTML全文

图 1 CNN模型结构

Figure 1. Basic structure of convolutional neural network basic structure

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图 2 本文搭建的CNN模型结构

Figure 2. Convolutional neural network structure

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图 3 预处理前后的波形图

Figure 3. Waveform before and after preprocessing

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图 4 预处理前后提取的脉冲时程

Figure 4. Pulse extracted before and after preprocessing

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图 5 评价指标曲线

Figure 5. Evaluation indication curve

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图 6 PI为0.15～0.85

Figure 6. PI is between 0.15 and 0.85

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图 7 PI小于0.15

Figure 7. PI is less than 0.15

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表 1 地震动记录信息及识别结果

Table 1. Ground motion record information and identification results

地震动编号	地震名称	PI（Baker，2007）	CNN识别结果
RSN180	Imperial Valley-06	0.23	速度脉冲
RSN292	Irpinia, Italy-01	0.13	速度脉冲
RSN1013	Northridge-01	0.08	速度脉冲
RSN1489	Chi-Chi	0.77	速度脉冲
RSN2628	Chi-Chi	0.04	速度脉冲
RSN3317	Chi-Chi	0.28	速度脉冲

下载: 导出CSV

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