摘要
本文采用人工神经网络技术,对新安煤矿小浪底水库下采煤导水裂隙带发育高度进行预测研究,选取了顶板岩性、顶板抗压强度、覆岩类型、倾角、覆岩厚度、泥岩比、煤层采厚等因素作为导水裂隙带预测模型的影响因子,建立了导水裂隙带高度的预测模型,准确判断了煤层开采后导水裂隙带的发育高度。本文的研究成果为新安煤矿合理设计小浪底水库下采煤的开采方式方法,提供了重要的参数依据和技术支撑。
Based on the Artificial Neural Network technology,this paper analyzed the height prediction method of the water conducted zone for mining under Xiaolangdi reservoir,selected lithology、compressive strength、types、thickness and the scale of mudstone in the overburden rock,obliquity and mining thickness of the coal seam as the main influence factors to establish the water conducted zone height prediction model,which forecasted the results effectively.The achievements of this study provided Xin＇an coal mine with some important parameters and technological supports for rational design of the mining ways and means.
出处
《中国矿业》
北大核心
2008年第3期96-99,共4页
China Mining Magazine
基金
国家重点基础研究发展计划("973"计划)(2007CB209401)
国家自然科学基金重点项目(50634050)
国家自然科学基金项目(40372123)资助
关键词
导水裂隙带
BP神经网络
水库下采煤
water conducted zone
BP neural network
mining under reservoir
