دورية أكاديمية
Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines
| العنوان: | Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines |
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| المؤلفون: | Pingan Peng, Zhengxiang He, Liguan Wang |
| المصدر: | Shock and Vibration, Vol 2019 (2019) |
| بيانات النشر: | Wiley, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Physics |
| مصطلحات موضوعية: | Physics, QC1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1070-9622 1875-9203 |
| Relation: | https://doaj.org/toc/1070-9622; https://doaj.org/toc/1875-9203 |
| DOI: | 10.1155/2019/5803184 |
| URL الوصول: | https://doaj.org/article/232a70e5302d49539f764cb308ca1c91 |
| رقم الأكسشن: | edsdoj.232a70e5302d49539f764cb308ca1c91 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10709622 18759203 |
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| DOI: | 10.1155/2019/5803184 |