Abstract
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.
Highlights
In the construction and production process of large-scale mines, all aspects of coal mine safety production need to accurately calculate the change in coal thickness [1]
Sun et al [17] used non-linear BP neural network technology to predict coal thickness by extracting seismic attributes in the wavelet domain of different frequency bands according to the changing characteristics of coal thickness
Based on the good denoising ability of Variational mode decomposition (VMD) and good prediction ability for complex sequences of long short-term memory neural network (LSTM), this paper proposes VMD-LSTM method for coal thickness prediction
Summary
In the construction and production process of large-scale mines, all aspects of coal mine safety production need to accurately calculate the change in coal thickness [1]. Sun et al [17] used non-linear BP neural network technology to predict coal thickness by extracting seismic attributes in the wavelet domain of different frequency bands according to the changing characteristics of coal thickness. Wu et al [18] combined the least square support vector machine (LSSVM) with the Kriging method to predict coal thickness They used the strong non-linear fitting and generalization capabilities of LSSVM to adaptively fit the experimental variogram, which overcame the disadvantages of the traditional variation function, such as difficulty in solving and strong subjectivity, and greatly improved the prediction accuracy. VMD is an adaptive, non-recursive decomposition method that can decompose signals into the sum of finite component signals [23] It is a new decomposition algorithm based on the Wiener filter, Hilbert transform, and heterodyne demodulation. When VMD is used to process non-stationary signals, it can effectively avoid the modealiasing effect and the endpoint effect caused by the EMD algorithm, and IMF is redefined as an amplitude modulation–frequency modulation signal
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
