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

Xin Wang, Tongjun Chen and Hui Xu
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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
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

Energies, 2020, vol. 13, issue 5, 1-14

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.

Keywords: TDC; thickness; prediction; deep belief network; dimensional reduction; 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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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