Coal Thickness Prediction Method Based on VMD and LSTM

Electronics

Yan Cheng¹  Xuemei Qi²  Yaping Huang²  Lei Yan²  Zhixiong Li³ 

[1] China National Administration of Coal Geology, Beijing 100038, China;School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China;Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea;

关键词: VMD;    LSTM;    coal thickness;    seismic attribute;   

DOI  :  10.3390/electronics11020232

来源: DOAJ

【 摘 要 】

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

【 授权许可】

Unknown