Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines
In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/5803184 |
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| _version_ | 1832564871705657344 |
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| author | Pingan Peng Zhengxiang He Liguan Wang |
| author_facet | Pingan Peng Zhengxiang He Liguan Wang |
| author_sort | Pingan Peng |
| collection | DOAJ |
| description | In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events. The visual scanning process is a time-consuming task and requires experience. Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) by using only Mel-frequency cepstral coefficient (MFCC) features extracted from the waveform. The detailed implementation of our proposed method is described. The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China). A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing. The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines. |
| format | Article |
| id | doaj-art-232a70e5302d49539f764cb308ca1c91 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-232a70e5302d49539f764cb308ca1c912025-02-03T01:09:57ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/58031845803184Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground MinesPingan Peng0Zhengxiang He1Liguan Wang2School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaIn order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events. The visual scanning process is a time-consuming task and requires experience. Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) by using only Mel-frequency cepstral coefficient (MFCC) features extracted from the waveform. The detailed implementation of our proposed method is described. The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China). A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing. The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines.http://dx.doi.org/10.1155/2019/5803184 |
| spellingShingle | Pingan Peng Zhengxiang He Liguan Wang Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines Shock and Vibration |
| title | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
| title_full | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
| title_fullStr | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
| title_full_unstemmed | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
| title_short | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
| title_sort | automatic classification of microseismic signals based on mfcc and gmm hmm in underground mines |
| url | http://dx.doi.org/10.1155/2019/5803184 |
| work_keys_str_mv | AT pinganpeng automaticclassificationofmicroseismicsignalsbasedonmfccandgmmhmminundergroundmines AT zhengxianghe automaticclassificationofmicroseismicsignalsbasedonmfccandgmmhmminundergroundmines AT liguanwang automaticclassificationofmicroseismicsignalsbasedonmfccandgmmhmminundergroundmines |