Application of Fuzzy Weights of Evidence Method in Mineral Resource Assessment for Gold in Zhenyuan District, Yunnan Province, China
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摘要: 采用模糊证据权方法和GeoDASGIS技术开展了镇沅(老王寨)及其邻区的金矿资源潜力评价.分别采用GeoDASGIS软件提供的局部奇异性分析技术、S-A异常分解技术、主成分分析技术、证据权、模糊证据权等技术对相关地球化学元素进行了系统的处理和分析.应用主成分分析方法确定了可能的2种不同成矿类型, 并采用主成分得分确定了组合异常点, 在此基础上分别采用普通证据权和模糊证据权方法编制了成矿后验概率图, 圈定了有利成矿地段.对比普通证据权方法与模糊证据权方法所得结果表明, 模糊证据权方法可减小图层离散化造成的有用信息损失, 提高预测结果精度.Abstract: The fuzzy weights of evidence method implemented in GeoDAS GIS was applied to delineate targets for exploration of gold mineral deposits in Zhenyuan mineral district, Yunnan Province, southwestern China. According to the mineral deposit model compiled by USGS, the mineral deposit type discovered in the area is determined as mesothermal gold deposit. Together with field observations the mineralization associated elements are determined, which include Au, As, Hg, Ag, Sb, Pb, and Cd. The singularity method and S-A methods provided in GeoDAS GIS were applied to delineate the weak anomalies and mixing anomalies related to gold mineral deposits. Principal component analysis method was utilized to analyze these elements to form two components (PC2 and PC3) which may reflect two different types of mineralization: PC2 dominated by Au-As-Hg-Co-Ni-Cu may be related to mesothermal deposits formed close to the contact of the ultramafic intrusions; whereas the PC3 dominated by Au-As-Hg-Ag-Pb may represent epithermal mineral deposits located in the sedimentary basin away from the ultramafic intrusions. The peaks of scores on these types of composite anomaly maps were delineated and used as training points for utilization of weights of evidence method and fuzzy weights of evidence method, respectively. 16 targeting areas were delineated using fuzzy weights of evidence method and were suggested for further exploration. The detailed comparison of fuzzy weights of evidence method with the ordinary weights of evidence method shows that the former can produce better results with less loss of useful information during construction of discrete evidential layers.
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图 1 信息提取与信息综合处理流程
Fig. 1. Flowchart showing procedures of spatial information extraction and integration
图 3 由主成分PC2和PC3构建的与金相关峰值点
a.PC2构建峰值点; b.PC3构建峰值点
Fig. 3. Peak points created from absolute high values of PC2 and PC3
图 4 JS_PC1与隶属度函数的关系
a.普通证据权法隶属度函数示意图; b.模糊证据权法隶属度函数示意图; 计算采用了第2类金相关峰值点集(来源于PC3)
Fig. 4. Fuzzy membership functions calculated using JS_PC1 and the second training point set
图 5 采用普通证据权法计算的金的后验概率
a.PC2峰值点为训练集, 最小值: 0.000 71, 最大值: 0.955 71, 均值: 0.008 50, 极差: 0.064 88, 标准差: 0.004 21;b.PC3峰值点为训练集, 最小值: 0.000 62, 最大值: 0.672 96, 均值: 0.010 79, 极差: 0.672 33, 标准差: 0.067 43
Fig. 5. Posterior probability map of Au created by using ordinary weights of evidence method
图 6 采用模糊证据权计算的金的后验概率图及资源潜力远景区预测
a.PC2峰值点为训练集, 最小值: 0.000 78, 最大值: 0.955 71, 均值: 0.072 24, 极差: 0.954 93, 标准差: 0.129 13;b.PC3峰值点为训练集, 最小值: 0.000 000 2, 最大值: 0.495 40, 均值: 0.056 51, 极差: 0.495 40, 标准差: 0.107 04
Fig. 6. Posterior probability map of Au and target areas favorable for Au mineral deposits delineated by fuzzy weights of evidence method
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