Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning
The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy valu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/9753028 |
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| _version_ | 1850110452333281280 |
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| author | Zilong Zhou Ruishan Cheng Xin Cai Dan Ma Chong Jiang |
| author_facet | Zilong Zhou Ruishan Cheng Xin Cai Dan Ma Chong Jiang |
| author_sort | Zilong Zhou |
| collection | DOAJ |
| description | The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN) as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM), naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC) curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140. |
| format | Article |
| id | doaj-art-461cbf98dec74607a0f335970067dd7b |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-461cbf98dec74607a0f335970067dd7b2025-08-20T02:37:49ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/97530289753028Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine LearningZilong Zhou0Ruishan Cheng1Xin Cai2Dan Ma3Chong Jiang4School of Resources and Safety Engineering, Central South University, Changsha 410000, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410000, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410000, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410000, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410000, ChinaThe automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN) as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM), naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC) curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.http://dx.doi.org/10.1155/2018/9753028 |
| spellingShingle | Zilong Zhou Ruishan Cheng Xin Cai Dan Ma Chong Jiang Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning Shock and Vibration |
| title | Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning |
| title_full | Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning |
| title_fullStr | Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning |
| title_full_unstemmed | Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning |
| title_short | Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning |
| title_sort | discrimination of rock fracture and blast events based on signal complexity and machine learning |
| url | http://dx.doi.org/10.1155/2018/9753028 |
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