证据权模型中两种预测单元划分方式对比

被引:9
作者
张道军 [1 ,2 ]
成秋明 [1 ]
左仁广 [1 ]
机构
[1] 中国地质大学地质过程与矿产资源国家重点实验室
[2] 中国地质大学资源学院
关键词
证据权模型; 单元划分; 矢量数据; 栅格数据; 数据综合; 资源评价;
D O I
暂无
中图分类号
P628 [数学勘探];
学科分类号
071208 [科技遗产与数字人文];
摘要
证据权模型作为一种数据综合方法已被广泛应用于矿产资源定量预测与评价。在模糊证据权基础上,发展了基于地质单元思想的矢量证据图层构建和数据综合方法,并通过实例作具体阐述:它以矿点缓冲区图层作为训练图层,以各证据变量图层在空间上的叠置所形成的唯一地质单元作为评价对象,统一计算各个证据变量的证据权重,进而基于地质单元进行证据综合和后验概率成图。与基于栅格(或规则格网)的模型不同,基于矢量证据权模型以具有明确地质内涵的地质单元(而非规则网格单元)为预测单元,易于解释,并且消除了边界误差;相比基于规则格网划分所得到的成矿单元,以矿床(点)缓冲区作为训练对象,提高了已知矿点的代表性。实例表明:若预测单元大小为初始栅格大小整数倍,各缓冲等级平均面积计算误差为0.26%,否则面积平均误差达到6%;即使在预测单元大小为初始栅格大小整数倍情况下,矿点平均计算误差也达到4.78%。因此,基于地质单元思想的证据权预测单元划分方法在精度上优于基于栅格或规则格网方法。
引用
收藏
页码:1040 / 1052
页数:13
相关论文
共 32 条
[1]
地下水环境脆弱性的研究 [D]. 
雷静 .
清华大学,
2002
[2]
Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia [J].
Porwal, A. ;
Gonzalez-Alvarez, I. ;
Markwitz, V. ;
McCuaig, T. C. ;
Mamuse, A. .
ORE GEOLOGY REVIEWS, 2010, 38 (03) :184-196
[3]
Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy) [J].
Cervi, Federico ;
Berti, Matteo ;
Borgatti, Lisa ;
Ronchetti, Francesco ;
Manenti, Federica ;
Corsini, Alessandro .
LANDSLIDES, 2010, 7 (04) :433-444
[4]
Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea.[J].Hyun-Joo Oh;Saro Lee.Natural Resources Research.2010, 2
[5]
A conditional dependence adjusted weights of evidence model [J].
Deng M. .
Natural Resources Research, 2009, 18 (4) :249-258
[6]
Gold predictivity mapping in French Guiana using an expert-guided data-driven approach based on a regional-scale GIS [J].
Cassard, D. ;
Billa, M. ;
Lambert, A. ;
Picot, J. -C. ;
Husson, Y. ;
Lasserre, J-L. ;
Delor, C. .
ORE GEOLOGY REVIEWS, 2008, 34 (03) :471-500
[8]
The tau model for data redundancy and information combination in Earth sciences: Theory and application [J].
Krishnan, Sunderrajan .
MATHEMATICAL GEOSCIENCES, 2008, 40 (06) :705-727
[9]
Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources [J].
Coolbaugh M.F. ;
Raines G.L. ;
Zehner R.E. .
Natural Resources Research, 2007, 16 (2) :199-207
[10]
Habitat quality assessment using Weights-of-Evidence based GIS modelling: The case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest.[J].Raul Romero-Calcerrada;Sandra Luque.Ecological Modelling.2006, 1