Intelligent Detection of Small Faults Using a Support Vector Machine
The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/19/6242 |
| _version_ | 1797516578179252224 |
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| author | Aiping Zeng Lei Yan Yaping Huang Enming Ren Tao Liu Hui Zhang |
| author_facet | Aiping Zeng Lei Yan Yaping Huang Enming Ren Tao Liu Hui Zhang |
| author_sort | Aiping Zeng |
| collection | DOAJ |
| description | The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD can effectively realize the attenuation of random noise and the seismic attributes extracted on this basis have a good correlation with the small fault. Through the analysis of the SVM algorithm and the fault forward model, it is proved that it is feasible to realize intelligent predictions of small faults by using seismic attributes as the input of a SVM. The fault prediction method using a SVM that is proposed in this paper has higher accuracy than the principal component analysis method, as the prediction results have important guiding significance and reference value for later coal mining. Therefore, the method presented in this paper can be used as a new intelligent method for small fault identification in coal fields. |
| first_indexed | 2024-03-10T07:02:51Z |
| format | Article |
| id | doaj.art-6e1a56bd49e34e3fb3d6acf3b9bcad6c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:02:51Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6e1a56bd49e34e3fb3d6acf3b9bcad6c2023-11-22T16:01:26ZengMDPI AGEnergies1996-10732021-09-011419624210.3390/en14196242Intelligent Detection of Small Faults Using a Support Vector MachineAiping Zeng0Lei Yan1Yaping Huang2Enming Ren3Tao Liu4Hui Zhang5Engineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaEngineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, ChinaEngineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, ChinaEngineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, ChinaThe small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD can effectively realize the attenuation of random noise and the seismic attributes extracted on this basis have a good correlation with the small fault. Through the analysis of the SVM algorithm and the fault forward model, it is proved that it is feasible to realize intelligent predictions of small faults by using seismic attributes as the input of a SVM. The fault prediction method using a SVM that is proposed in this paper has higher accuracy than the principal component analysis method, as the prediction results have important guiding significance and reference value for later coal mining. Therefore, the method presented in this paper can be used as a new intelligent method for small fault identification in coal fields.https://www.mdpi.com/1996-1073/14/19/6242variable mode decompositionsupport vector machinecoal seamsmall faultseismic attribute |
| spellingShingle | Aiping Zeng Lei Yan Yaping Huang Enming Ren Tao Liu Hui Zhang Intelligent Detection of Small Faults Using a Support Vector Machine Energies variable mode decomposition support vector machine coal seam small fault seismic attribute |
| title | Intelligent Detection of Small Faults Using a Support Vector Machine |
| title_full | Intelligent Detection of Small Faults Using a Support Vector Machine |
| title_fullStr | Intelligent Detection of Small Faults Using a Support Vector Machine |
| title_full_unstemmed | Intelligent Detection of Small Faults Using a Support Vector Machine |
| title_short | Intelligent Detection of Small Faults Using a Support Vector Machine |
| title_sort | intelligent detection of small faults using a support vector machine |
| topic | variable mode decomposition support vector machine coal seam small fault seismic attribute |
| url | https://www.mdpi.com/1996-1073/14/19/6242 |
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