A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield

Xiaobo Lin, Pingsong Zhang (), Fanbin Meng and Chang Liu
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Xiaobo Lin: School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
Pingsong Zhang: School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
Fanbin Meng: Geophysical Prospecting Research Institute, CNACG, Zhuozhou 072750, China
Chang Liu: School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China

Energies, 2022, vol. 15, issue 19, 1-19

Abstract: The precise prediction of coal seam thickness in operating mines is crucial for the construction of transparent mines. Geological borehole data or a small amount of seismic information is frequently used in traditional coal seam thickness prediction methods; however, these methods have poor precision. In this study, we introduced a model for predicting coal seam thickness based on the comprehensive preference for seismic attribute combination (CPSAC) and the least squares support vector machine (LS-SVM) optimized by the whale optimization algorithm (WOA). We used the CPSAC to modify the mass disturbed data in the seismic attribute data to predict the coal seam thickness. To achieve this the sample size was reduced by optimizing the seismic attribute combinations, and the modified attribute data was entered into the LS-SVM., Furthermore, to create an accurate prediction model for coal thickness, we employed the WOA to determine the optimal penalty coefficient and kernel coefficient of the LS-SVM. An empirical case study was conducted in the northeast mining area of the ZJ mine in the Huainan coalfield. The coal thickness of two mining faces in this research area were estimated and compared, demonstrating the proposed method’s high prediction accuracy. The proposed method has guiding implications for developing an accurate mining geological model and facilitating the accurate use of coal resources.

Keywords: comprehensive preference; seismic attributes combination; WOA–LS-SVM; prediction model; 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: 2022
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