| 基于卷积神经网络的速度大脉冲识别方法研究 |
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| 引用本文： | 牛志辉,陈波,卜春尧.基于卷积神经网络的速度大脉冲识别方法研究[J].震灾防御技术,2021,16(3):485-491. |
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| 作者姓名： | 牛志辉 陈波 卜春尧 |
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| 作者单位： | 中国地震局地球物理研究所，北京 100081 |
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| 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） |
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| 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。
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| 关 键 词： | 强地震动 速度脉冲 卷积神经网络（CNN） 图像识别 |
| 收稿时间： | 2021-03-30 |
Identification of Pulse-like Strong Ground Motions Based on Convolution Neural Network |
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| Niu Zhihui,Chen Bo,Bu Chunyao.Identification of Pulse-like Strong Ground Motions Based on Convolution Neural Network[J].Technology for Earthquake Disaster Prevention,2021,16(3):485-491. |
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| Authors: | Niu Zhihui Chen Bo Bu Chunyao |
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| Institution: | Institute of Geophysics, China Earthquake Administration, Beijing 100081, China |
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| 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. |
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| Keywords: | Strong ground motion Velocity pulse Convolution neural network Image recognition |
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