沟谷区浅埋煤层矿压显现时空特征辨识及预测方法研究

doi:10.3724/1000-6915.jrme.2025.0057

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Abstract The Western mining area has emerged as a crucial “ballast stone” for ensuring China?s energy security. Accurately identifying and predicting the spatiotemporal characteristics of mining pressure is essential for the safe extraction of shallow coal seams in this region. The interplay between temporal stress evolution in the mining area and spatial differences in the support system, due to the absence of key layers in the uphill section, complicates the manifestation of mining pressure in shallow valley-buried working faces. To address the low predictive accuracy caused by the nonlinear and differentiated distribution of stope data in these shallow coal seam working faces, this paper proposes an intelligent prediction method for ground pressure in valley mining faces. This method factors in the physical constraints of the “inclined bench rock beam” type and the spatiotemporal characteristics of support pressure data, while also accounting for the absence of the main key stratum. Initially, a theoretical model of the “inclined step rock beam” with missing key stratum is established. The model integrates data analysis techniques such as principal component analysis, kernel density estimation, and the DBSCAN clustering algorithm to clarify the spatiotemporal correlation between complex static operating parameters—such as slope angle and working face location—and the local strengthening effect of support pressure. Subsequently, an improved TFT architecture deep learning prediction model for mining pressure is developed, incorporating spatiotemporal correlation features and static metadata. Results indicate that the absence of the main key stratum is the primary controlling factor for mining pressure manifestation in the uphill section of shallow buried working faces in valley areas. Stress concentration occurs in the middle of the working face during this process. By introducing static metadata such as slope angle and working face location, along with the spatiotemporal mechanism of differential support pressure, the prediction accuracy of mining pressure is significantly improved. Compared with particle swarm optimization support vector machine (PSO-SVM), LSTM, and other mining pressure prediction algorithms, the R² value increased by up to 28% and the RMSE decreased by up to 64.9%. This provides a valuable reference for recognizing spatiotemporal characteristics and issuing intelligent warnings for mining pressure in shallow buried working faces in western valley areas.

Key words： mining engineering;valley area;shallow buried coal seam;mining pressure spatiotemporal fusion;intelligent prediction

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	Articles by authors
	XU Huicong1
	2
	LAI Xingping1
	2
	SHAN Pengfei1
	2
	GUO Zhong?an3
	XUE Ke3
	YAN Zhongming2
	4
	WANG Huachuan1
	5
	XU Gang2
	MENG Zheng2

Cite this article:

XU Huicong1,2,LAI Xingping1, et al. Research on spatiotemporal characteristics perception and prediction methods of shallow buried coal seam mining pressure in valley areas[J]. , 2025, 44(6): 1450-1465.

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https://rockmech.whrsm.ac.cn/EN/10.3724/1000-6915.jrme.2025.0057 OR https://rockmech.whrsm.ac.cn/EN/Y2025/V44/I6/1450

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