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Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study

Author

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  • Xin Wang

    (Department of Data Science, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
    Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China)

  • Tongjun Chen

    (Department of Geophysics, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Hui Xu

    (Department of Data Science, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
    Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Thickness of tectonically deformed coal (TDC) has positive correlations with the susceptible gas outbursts in coal mines. To predict the TDC thickness of the coalbed, we proposed a prediction method using seismic attributes based on the deep belief network (DBN) and dimensionality reduction. Firstly, we built a DBN prediction model using the extracted attributes from a synthetic seismic section. Next, we transformed the possibly correlated seismic attributes into principal components through principal components analysis. Then, we compared the true TDC thickness with the predicted TDC thicknesses to evaluate the prediction accuracy of different models, i.e., a DBN model, a support vector machine model, and an extreme learning machine model. Finally, we used the DBN model to predict the TDC thickness of coalbed No. 8 in an operational coal mine based on synthetic experiments. Our studies showed that the predicted distribution of TDC thickness followed the regional characteristics of TDC development well and was positively correlated with the burial depth, coalbed thickness, and tectonic development. In summary, the proposed DBN model provided a reliable method for predicting TDC thickness and reducing gas outbursts in coal mine operations.

Suggested Citation

  • Xin Wang & Tongjun Chen & Hui Xu, 2020. "Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study," Energies, MDPI, vol. 13(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1169-:d:328330
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Jun Lu & Yun Wang & Jingyi Chen, 2018. "Detection of Tectonically Deformed Coal Using Model-Based Joint Inversion of Multi-Component Seismic Data," Energies, MDPI, vol. 11(4), pages 1-17, April.
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    Cited by:

    1. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    2. Xiaobo Lin & Pingsong Zhang & Fanbin Meng & Chang Liu, 2022. "A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield," Energies, MDPI, vol. 15(19), pages 1-19, October.
    3. Anmin Wang & Daiyong Cao & Yingchun Wei & Zhifei Liu, 2020. "Macromolecular Structure Controlling Micro Mechanical Properties of Vitrinite and Inertinite in Tectonically Deformed Coals—A Case Study in Fengfeng Coal Mine of Taihangshan Fault Zone (North China)," Energies, MDPI, vol. 13(24), pages 1-23, December.

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