Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics
Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/6059239 |
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| _version_ | 1832565089702510592 |
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| author | Baolin Li Nan Li Enyuan Wang Xuelong Li Zhibo Zhang Xin Zhang Yue Niu |
| author_facet | Baolin Li Nan Li Enyuan Wang Xuelong Li Zhibo Zhang Xin Zhang Yue Niu |
| author_sort | Baolin Li |
| collection | DOAJ |
| description | Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines. |
| format | Article |
| id | doaj-art-3ebfdffc24364c85b581d673809241ac |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-3ebfdffc24364c85b581d673809241ac2025-02-03T01:09:13ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/60592396059239Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform CharacteristicsBaolin Li0Nan Li1Enyuan Wang2Xuelong Li3Zhibo Zhang4Xin Zhang5Yue Niu6State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaGenerally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines.http://dx.doi.org/10.1155/2017/6059239 |
| spellingShingle | Baolin Li Nan Li Enyuan Wang Xuelong Li Zhibo Zhang Xin Zhang Yue Niu Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics Shock and Vibration |
| title | Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics |
| title_full | Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics |
| title_fullStr | Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics |
| title_full_unstemmed | Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics |
| title_short | Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics |
| title_sort | discriminant model of coal mining microseismic and blasting signals based on waveform characteristics |
| url | http://dx.doi.org/10.1155/2017/6059239 |
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