Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning
Joint Authors
Ma, Dan
Jiang, Chong
Cai, Xin
Zhou, Zilong
Cheng, Ruishan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhou, Zilong& Cheng, Ruishan& Cai, Xin& Ma, Dan& Jiang, Chong. 2018. Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning. Shock and Vibration،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215576
Modern Language Association (MLA)
Zhou, Zilong…[et al.]. Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning. Shock and Vibration No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1215576
American Medical Association (AMA)
Zhou, Zilong& Cheng, Ruishan& Cai, Xin& Ma, Dan& Jiang, Chong. Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215576
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215576