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

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.

A sorting method for coal and gangue based on surface grayness and glossiness

“…Classification models based on statistical theory have recently been developed and machine learning and deep learning have been widely used in the coal industry (Gao et al 2020;Lin et al 2022). Unfortunately, the deep learning model needs image data to support it.…”