基于BP神经网络的微震多属性预测工作面煤厚判定方法

doi:10.19286/j.cnki.cci.2024.01.011

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Abstract Microseismic monitoring technology was widely used in coal mines and non-coal mines in china and abroad, which played an important role in predicting rock burst, rock burst, prevention of water damage, deep stope stability and roof caving and other sudden disasters. Based on the microseismic monitoring results of No.15249N Face in Jiulong Mine of Hanxing mining area, this paper extracted ten kinds of microseismic attribute data. Through the optimization and error analysis of microseismic attributes, five kinds of source parameters including moment magnitude, sliding displacement, volume change potential, energy and static stress drop, were optimized. Combined with roadway exposure and drilling constraint method, BP artificial neural network method was used to calculate. The optimal attribute order and hidden node number were obtained by exhaustive search ( ES ) algorithm and cut and trial increasing method. A microseismic multi-attribute coal seam thickness prediction model based on BP neural network was established. The error analysis and similar area determination of the prediction model were carried out. Combined with the actual geological conditions, it was verified that the model had a good application effect in the determination of coal thickness in the working face.

Key words： BP neural network microseism prediction of coal thickness

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	Articles by authors
	Guo Xueting1
	2
	Wang Peng1
	2
	Wang Xiaoyu3

Cite this article:

Guo Xueting1,2,Wang Peng1等. Coal thickness determination method of microseismic multi-attribute prediction working face based on BP neural network[J]. CCI, 2024, 47(1): 48-54.

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http://www.mtyhg.com.cn/EN/10.19286/j.cnki.cci.2024.01.011 OR http://www.mtyhg.com.cn/EN/Y2024/V47/I1/48

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