Microseismic Signal Classification Based on Artificial Neural Networks
The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and wavef...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6697948 |
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| _version_ | 1832548671340675072 |
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| author | Chong-wei Xin Fu-xing Jiang Guo-dong Jin |
| author_facet | Chong-wei Xin Fu-xing Jiang Guo-dong Jin |
| author_sort | Chong-wei Xin |
| collection | DOAJ |
| description | The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%. |
| format | Article |
| id | doaj-art-429fe229c1f14d05869c0a332081b9d5 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-429fe229c1f14d05869c0a332081b9d52025-02-03T06:13:19ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66979486697948Microseismic Signal Classification Based on Artificial Neural NetworksChong-wei Xin0Fu-xing Jiang1Guo-dong Jin2Civil and Resource Engineering School, University of Science and Technology Beijing, Beijing 100083, ChinaCivil and Resource Engineering School, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Anke Industrial Technology Co., Ltd., Beijing 100083, ChinaThe classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.http://dx.doi.org/10.1155/2021/6697948 |
| spellingShingle | Chong-wei Xin Fu-xing Jiang Guo-dong Jin Microseismic Signal Classification Based on Artificial Neural Networks Shock and Vibration |
| title | Microseismic Signal Classification Based on Artificial Neural Networks |
| title_full | Microseismic Signal Classification Based on Artificial Neural Networks |
| title_fullStr | Microseismic Signal Classification Based on Artificial Neural Networks |
| title_full_unstemmed | Microseismic Signal Classification Based on Artificial Neural Networks |
| title_short | Microseismic Signal Classification Based on Artificial Neural Networks |
| title_sort | microseismic signal classification based on artificial neural networks |
| url | http://dx.doi.org/10.1155/2021/6697948 |
| work_keys_str_mv | AT chongweixin microseismicsignalclassificationbasedonartificialneuralnetworks AT fuxingjiang microseismicsignalclassificationbasedonartificialneuralnetworks AT guodongjin microseismicsignalclassificationbasedonartificialneuralnetworks |