Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Abstract

Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

An Experimental and Statistical Study on Rebar Corrosion Considering the Temperature Effect Using Gaussian Process Regression

“…When predicting in the range of temperature, the trend was similar to real data fitting, and the grey areas showed small gaps from the trend line. The grey areas in Figure 5 are the 95% confidence intervals and the dots are the predicted data in the range of the given data [39]. The given data refers to the data used in the GPR fitting.…”

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

“…The deep integration of seismic multi-attribute and mathematical methods marked a new stage in coal thickness prediction research. Artificial neural networks, fuzzy neural networks, deep belief networks, extreme value learning machines, and support vector machines (SVMs) have been applied to studies on coal thickness prediction [17][18][19][20][21][22][23][24][25]. However, these methods have not been extensively applied in the coal mining industry because of limitations in learning speed and modeling capacity.…”