Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning
The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine...
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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/10.1155/2021/9544997 |
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| _version_ | 1832549162599579648 |
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| author | Rui Liu |
| author_facet | Rui Liu |
| author_sort | Rui Liu |
| collection | DOAJ |
| description | The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea. |
| format | Article |
| id | doaj-art-5be8f503006447ee80511d2ec7a9d045 |
| 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-5be8f503006447ee80511d2ec7a9d0452025-02-03T06:12:00ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/95449979544997Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised LearningRui Liu0School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaThe feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.http://dx.doi.org/10.1155/2021/9544997 |
| spellingShingle | Rui Liu Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning Shock and Vibration |
| title | Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning |
| title_full | Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning |
| title_fullStr | Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning |
| title_full_unstemmed | Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning |
| title_short | Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning |
| title_sort | research on feature fusion method of mine microseismic signal based on unsupervised learning |
| url | http://dx.doi.org/10.1155/2021/9544997 |
| work_keys_str_mv | AT ruiliu researchonfeaturefusionmethodofminemicroseismicsignalbasedonunsupervisedlearning |