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A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines
The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436190/ https://www.ncbi.nlm.nih.gov/pubmed/32756357 http://dx.doi.org/10.3390/s20154322 |
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author | Wei, Hui Shu, Weiwei Dong, Longjun Huang, Zhongying Sun, Daoyuan |
author_facet | Wei, Hui Shu, Weiwei Dong, Longjun Huang, Zhongying Sun, Daoyuan |
author_sort | Wei, Hui |
collection | PubMed |
description | The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis. |
format | Online Article Text |
id | pubmed-7436190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74361902020-08-24 A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines Wei, Hui Shu, Weiwei Dong, Longjun Huang, Zhongying Sun, Daoyuan Sensors (Basel) Article The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis. MDPI 2020-08-03 /pmc/articles/PMC7436190/ /pubmed/32756357 http://dx.doi.org/10.3390/s20154322 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wei, Hui Shu, Weiwei Dong, Longjun Huang, Zhongying Sun, Daoyuan A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title | A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title_full | A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title_fullStr | A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title_full_unstemmed | A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title_short | A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines |
title_sort | waveform image method for discriminating micro-seismic events and blasts in underground mines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436190/ https://www.ncbi.nlm.nih.gov/pubmed/32756357 http://dx.doi.org/10.3390/s20154322 |
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