Intelligent Detection of Small Faults Using a Support Vector Machine

Aiping Zeng, Lei Yan, Yaping Huang, Enming Ren, Tao Liu and Hui Zhang
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Aiping Zeng: Engineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, China
Lei Yan: School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
Yaping Huang: School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
Enming Ren: Engineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, China
Tao Liu: Engineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, China
Hui Zhang: Engineering Laboratory for Deep Mine Rockburst Disaster Assessment, Jinan 250104, China

Energies, 2021, vol. 14, issue 19, 1-16

Abstract: 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.

Keywords: variable mode decomposition; support vector machine; coal seam; small fault; seismic attribute (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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