Omar M. Saad, M. Sami Soliman, Yangkang Chen, Abutaleb A. Amin, H. E. Abdelhafiez
{"title":"基于紧凑卷积变压器的胶囊神经网络识别采石场爆炸地震","authors":"Omar M. Saad, M. Sami Soliman, Yangkang Chen, Abutaleb A. Amin, H. E. Abdelhafiez","doi":"10.1785/0220230101","DOIUrl":null,"url":null,"abstract":"Abstract Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time–frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, Visual Geometry Group (VGG), and AlexNet networks. Finally, the proposed method is demonstrated to enjoy a high-generalization ability through a real-time monitoring experiment.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"63 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Capsule Neural Network Guided by Compact Convolutional Transformer for Discriminating Earthquakes from Quarry Blasts\",\"authors\":\"Omar M. Saad, M. Sami Soliman, Yangkang Chen, Abutaleb A. Amin, H. E. Abdelhafiez\",\"doi\":\"10.1785/0220230101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time–frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, Visual Geometry Group (VGG), and AlexNet networks. 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引用次数: 0
摘要
采石场爆炸等非地震事件的错误分类可能会污染地震目录。局部地震有时具有与采石场爆炸相似的特征,这使得人工判别困难且不可靠。因此,我们建议使用紧凑卷积变压器(CCT)和胶囊神经网络来区分地震和采石场爆炸。首先,提取了纵波到达时间前后10 s和50 s的60 s三通道地震图。然后,利用连续小波变换将时间序列数据转换为时频域(尺度图)。然后,我们利用CCT网络从输入尺度图中提取最重要的特征。利用胶囊神经网络,采用协议路由方法(动态路由)提取提取特征之间的空间关系。胶囊神经网络为地震和采石场爆炸分类提取不同的数字向量,具有较好的分类精度。利用埃及地震台网记录的地震数据集对所提出的算法进行了评估。数据集分为80%用于训练,20%用于测试。虽然数据集是不平衡的,但所提出的算法显示了令人满意的结果。该算法的测试准确率为97.31%。查准率为97.23%,查全率为98.83%,f1得分为98.02%。此外,该算法优于传统的深度学习模型,如卷积神经网络、ResNet、Visual Geometry Group (VGG)和AlexNet网络。最后,通过实时监测实验验证了该方法具有较高的泛化能力。
Capsule Neural Network Guided by Compact Convolutional Transformer for Discriminating Earthquakes from Quarry Blasts
Abstract Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time–frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, Visual Geometry Group (VGG), and AlexNet networks. Finally, the proposed method is demonstrated to enjoy a high-generalization ability through a real-time monitoring experiment.