东昆仑某铜镍矿床矿石体重与品位回归模型探讨
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摘要
矿石体重值为储量估算中的重要参数。以东昆仑夏日哈木铜镍矿床小体重测试数据为基础,运用数学地质原理及SPSS软件探讨了小体重值与Ni、Cu、Co元素品位之间的关系,建立了以Ni、Co元素品位与小体重值的回归模型。预测体重与实际测试体重平均误差为6.08%,建立的回归模型可为储量计算提供科学依据。
The ore density is a crucial index to evaluate the ore reserves. In this study, the ore density data of Xiarihamu Cu-Ni deposit in eastern Kunlun Mountains has been used to analyze the relationship between the ore density and element grade of Ni, Cu and Co by applying mathematical geology principles and SPSS statistical analysis. As a result, a regression model has been proposed to predict ore density according to the element grade of Ni and Co. By using this regression model, the relative error of predicted ore density is about 6.08%. It is thus concluded that the proposed regression model is capable of ore reserve prediction for future applications.
The ore density is a crucial index to evaluate the ore reserves. In this study, the ore density data of Xiarihamu Cu-Ni deposit in eastern Kunlun Mountains has been used to analyze the relationship between the ore density and element grade of Ni, Cu and Co by applying mathematical geology principles and SPSS statistical analysis. As a result, a regression model has been proposed to predict ore density according to the element grade of Ni and Co. By using this regression model, the relative error of predicted ore density is about 6.08%. It is thus concluded that the proposed regression model is capable of ore reserve prediction for future applications.
引文
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[3] 何兴江,张信贵.基于spss的城市区域地下水变异Factor Analysis过程[J].地质与勘探, 2006,42(1): 93-95.
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[5] 严利伟,王昌南,唐高林,等.多元线性回归计算矿石体重方法的优化——以某铜锌矿为例[J].四川地质学报,2017,37(2): 331-334.
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[7] 李世金, 孙丰月,高永旺, 等.小岩体成大矿理论指导与实践—青海东昆仑夏日哈木铜镍矿找矿突破的启示及意义[J].西北地质, 2012, 45(4): 185-191.
[8] 张金玲, 李领贵, 杨启安, 等. 基于钻孔信息的夏日哈木铜镍矿床地质、地球化学特征及意义[J]. 科学技术与工程, 2016, 16(18): 123-128.
[9] 张景平, 李社. 基于SPSS的矿石小体重与品位的多元回归分析模型的构建[J]. 东华理工大学学报(自然科学版), 2011, 34(1): 62-66.
[10] 林喜. 矿石体重的多元线性回归分析及其检验[J]. 福建地质, 2009, 29: 157-163.
[2] 冯适安.多金属矿床中矿石体重与金属品位的关系[J].湖南地质,1983,2(2): 58-61.
[3] 何兴江,张信贵.基于spss的城市区域地下水变异Factor Analysis过程[J].地质与勘探, 2006,42(1): 93-95.
[4] 候景儒,黄竞先.地质统计学及其在矿产储量计算中的应用[M].北京:地质出版社,1982.
[5] 严利伟,王昌南,唐高林,等.多元线性回归计算矿石体重方法的优化——以某铜锌矿为例[J].四川地质学报,2017,37(2): 331-334.
[6] 刘晓石,陈鸿建,何腊梅,等.概率论与数理统计(第二版)[M].北京:科学出版社,2005.
[7] 李世金, 孙丰月,高永旺, 等.小岩体成大矿理论指导与实践—青海东昆仑夏日哈木铜镍矿找矿突破的启示及意义[J].西北地质, 2012, 45(4): 185-191.
[8] 张金玲, 李领贵, 杨启安, 等. 基于钻孔信息的夏日哈木铜镍矿床地质、地球化学特征及意义[J]. 科学技术与工程, 2016, 16(18): 123-128.
[9] 张景平, 李社. 基于SPSS的矿石小体重与品位的多元回归分析模型的构建[J]. 东华理工大学学报(自然科学版), 2011, 34(1): 62-66.
[10] 林喜. 矿石体重的多元线性回归分析及其检验[J]. 福建地质, 2009, 29: 157-163.