摘要
采用地质岩芯RQD智能识别方法对三山岛金矿西岭矿区118条地质钻孔岩芯RQD进行识别,得到矿区范围内地质钻孔岩芯RQD数据库;对钻孔岩芯RQD块段长度分析并建立组合样品,统计所有钻孔RQD数据变异性。运用地质统计学方法对钻孔岩芯RQD分析并建立RQD数据半变异函数,得到块金值0.8、基台值0.58、变程80 m的球状模型。采用普通克里金插值法,设置搜索参数对块体模型赋值。块体模型内块体超310万,RQD最小估值为3.7,RQD最大估值99.0,均值45.1,标准差11.5,交叉验证显示原样RQD数值与克里金插值法估值结果相差很小,据此建立西岭矿区RQD块体模型,实现西岭矿区RQD岩体质量分布的三维可视化,用于矿山岩体质量评价,为分析采场及巷道围岩稳定性、合理选择采场及巷道的支护形式及其参数提供基础数据。
The RQD of 118 geological boreholes in Xiling mining area of Sanshandao Gold mine was identified by the intelligent identification method of geological core RQD,and the RQD database of geological boreholes in the mining area was obtained.The RQD block length of drill core was analyzed and combined samples were established.The RQD data variability of all boreholes was calculated.Geostatistics method is used to analyze the RQD of drill core and establish the semi-variation function of RQD data.The spherical model with nugget value 0.8,abutment value 0.58 and range 80m is obtained.By using Kriging interpolation,search parameters are set to assign values to the block model.There are more than 3.1 million blocks in the block model.The minimum RQD estimate is 3.7.The maximum RQD estimate is 99.0.The mean is 45.1 and the standard deviation is 11.5.Cross-validation shows that the original RQD value has little difference with the Kriging interpolation.Based on this,the RQD block model of Xiling Mining area is established to realize the 3D visualization of RQD rock mass distribution in Xiling mining area,which can be used for the evaluation of mine rock mass quality and provide basic data for the analysis of the stability of surrounding rock of stope and roadway,reasonable selection of stope and roadway support forms and their parameters.
作者
王照亚
白夜
张武
王立君
杨尚欢
赵兴东
WANG Zhao-ya;BAI Ye;ZHANG Wu;WANG Li-jun;YANG Shang-huan;ZHAO Xing-dong(Sanshandao Gold Mine of Shandong Gold Mining(Laizhou)Co.,Ltd.,Laizhou 261400,China;Laboratory for Safe Mining in Deep Metal Mine,Northeastern University,Shenyang 110819,China)
出处
《有色矿冶》
2023年第3期12-17,共6页
Non-Ferrous Mining and Metallurgy
基金
国家自然科学基金重点项目(52130403)资助
2023年辽宁省中央引导地方科技发展资金计划项目资助。