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

• Main Authors: Xin Wang, Tongjun Chen, Hui Xu
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Energies
• Subjects: tdc; thickness; prediction; deep belief network; dimensional reduction; seismic attribute
• Online Access: https://www.mdpi.com/1996-1073/13/5/1169

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.