Zhang, G, Qi, A, Wenhui, K and Haijun, L (2019) Coal seam thickness prediction based on transition probability of structural elements. Applied Sciences, 9 (6). ISSN 2076-3417

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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 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. Then, 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 BP neural networks, support vector machine and Gaussian process regression methods.

Item Type:	Article
Subjects:	T Technology > TN Mining engineering. Metallurgy
Divisions:	Engineering
Publisher:	MDPI AG
Date Deposited:	15 Mar 2019 16:21
Last Modified:	03 Sep 2021 21:08
DOI or ID number:	10.3390/app9061144
URI:	https://researchonline.ljmu.ac.uk/id/eprint/10344

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