Evaluation of Copper Mineral Resource Potential Using Concentration–Area Fractal Model and Fuzzy Evidence Weighting: A Case Study of the Jiurui Region in Jiangxi
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摘要:
中国江西省的九瑞地区是长江中下游成矿带中最重要的铜矿产地之一，其中花岗闪长斑岩与铜成矿关系密切。基于水系沉积物与矿化相关的信息，采用因子分析（FA）、浓度–面积分形法（C–A）和模糊证据权方法（FWofE）相结合建立成矿潜力预测模型。使用因子分析处理包含32个元素的255份水系沉积物样本数据，找到能够指示铜矿化的组合元素（即主因子）。采用多重分形反距离加权插值法（MIDW）创建主因子得分栅格图并用C–A分形模型提取与铜矿化相关的地化异常。将得到和铜矿化相关的地球化学异常图与地质、遥感解译数据相结合，应用模糊证据权方法建立预测模型。结果表明：已知铜矿床位于圈定预测概率高值区，且受花岗闪长斑岩和断裂的分布共同控制；除已知铜矿床区域外，圈定的3个一级远景区域内也具有较高的概率，值得进一步铜勘查找矿工作的进行。
Abstract:The Jiurui region in Jiangxi Province, China, is one of the most significant copper mining areas in the middle and lower reaches of the Yangtze River mineralization belt, with a close relationship between granodiorite porphyry and copper mineralization. In this study, a predictive model for mineralization potential was established by combining factor analysis (FA), concentration-area (C-A) fractal method, and fuzzy weight of evidence (FWofE) based on information related to stream sediment and mineralization. ϕfactor analysis was applied to a dataset of 255 stream sediment samples containing 32 elements to identify combinations of elements (principal factors) indicative of copper mineralization. κ the principal factor scores were interpolated using the multiple inverse distance weighted (MIDW) method to create a raster map, and the C-A fractal model was employed to extract geochemical anomalies associated with copper mineralization. λ the geochemical anomaly map related to copper mineralization was integrated with geological and remote sensing interpretation data, and a predictive model was established using the fuzzy weight of evidence method. The results indicated that: known copper deposits are located within high-probability zones defined by the model and are influenced by the distribution of granodiorite porphyry and faults; in addition to the known copper deposit areas, three primary prospective areas identified within the defined regions also exhibit a high probability, meriting further exploration efforts for copper prospecting.
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图 1 长江中下游成矿带简易地质图（据Pan et al.，1999修）
Figure 1.
图 2 九瑞铜矿区地质图（据Yang et al.，2011修）
Figure 2.
表 1 32种元素的检出限表
Table 1. Detection limits of 32 elements
序号 元素 检出限 序号 元素 检出限 1 Ag 0.01 17 Mo 0.5 2 As 2.82 18 Nb 5 3 Au 0.0003 19 Ni 5 4 B 5 20 P 30 5 Ba 10 21 Pb 5.4 6 Be 0.5 22 Sb 0.2 7 Bi 0.16 23 Sn 0.14 8 Cd 0.1 24 Sr 5 9 Co 1 25 Th 5.1 10 Cr 7.2 26 Ti 30 11 Cu 1 27 U 1 12 F 13 28 V 2 13 Hg 0.01 29 W 0.5 14 La 10 30 Y 10 15 Li 5 31 Zn 10 16 Mn 30 32 Zr 10 注：元素含量为10-6。 
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表 2 R型因子分析的正交旋转因子载荷矩阵表
Table 2. Orthometric rotating factor loading matrix for R-factor analysis
变量 因子载荷 F1 F2 F3 F4 F5 F6 F7 Ag 0.101 −0.031 0.954 −0.065 0.030 0.039 −0.062 As 0.059 0.329 −0.099 0.854 −0.039 −0.027 −0.203 Au −0.012 0.717 −0.041 0.007 −0.026 0.005 −0.069 B −0.303 −0.072 −0.021 −0.072 0.716 0.163 −0.104 Ba 0.101 −0.031 0.954 −0.065 0.030 0.039 −0.062 Be 0.910 −0.023 0.175 0.068 0.015 0.170 −0.016 Bi −0.025 0.941 −0.006 0.062 −0.017 −0.032 0.007 Cd 0.055 0.249 −0.098 0.917 −0.099 −0.014 0.102 Co 0.767 −0.006 0.093 0.435 0.224 −0.021 −0.112 Cr 0.831 0.001 0.007 −0.061 −0.013 0.082 0.036 Cu −0.089 0.786 0.022 0.324 −0.022 0.014 0.288 F 0.835 0.043 −0.057 −0.051 −0.087 0.102 0.123 Hg 0.063 0.221 0.023 0.196 0.100 0.173 −0.443 La 0.235 −0.093 0.068 −0.125 0.153 0.760 0.047 Li 0.890 −0.019 0.124 0.013 0.059 0.177 −0.038 Mn 0.489 0.005 0.065 0.328 0.198 0.016 −0.212 Mo −0.009 0.489 0.015 0.229 0.015 0.044 0.679 Nb 0.254 0.104 0.118 −0.045 0.611 0.123 0.029 Ni 0.875 −0.069 0.049 −0.070 −0.026 0.181 0.052 P 0.464 −0.049 0.417 0.025 −0.226 −0.218 0.100 Pb 0.075 0.918 −0.014 0.050 −0.002 −0.026 −0.157 Sb 0.048 0.926 −0.062 0.169 0.022 −0.059 −0.053 Sn 0.274 0.287 0.064 0.053 −0.094 0.572 0.068 Sr 0.209 0.058 0.736 −0.077 −0.411 −0.093 0.068 Th 0.332 −0.015 −0.241 0.010 0.603 −0.116 0.094 Ti 0.569 0.011 0.586 0.006 0.252 −0.002 −0.110 U 0.170 −0.022 −0.193 −0.058 0.073 0.089 0.289 V 0.931 0.042 0.146 0.049 0.036 0.134 0.032 W −0.077 0.657 0.171 0.144 0.064 −0.022 0.444 Y 0.108 −0.216 −0.181 0.031 0.152 0.739 −0.141 Zn 0.026 0.049 −0.017 0.959 −0.055 −0.023 0.041 Zr −0.570 −0.070 −0.166 −0.098 0.546 0.097 0.007 注：该因子分析采用的提取方法为主成分分析法，旋转方法为Kaiser标准化最大方差法，旋转在七次迭代后已经收敛。
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表 3 各证据层隶属度表（MSF）及模糊证据权重计算表
Table 3. Table of membership of each evidence layer (MSF) and calculation of fuzzy weights of evidence
缓冲距离分类 主要赋矿地层 断裂 花岗闪长斑岩 绿泥石化蚀变 地球化学异常分类 C-A分形模型 分类值 缓冲
距离（m）隶属度 证据
权重隶属度 证据
权重隶属度 证据
权重隶属度 证据
权重分类值 C-A分形 隶属度 证据
权重1 100 1 0.60 1 0.37 1 4.06 1 2.05 1 高异常 1 2.06 2 200 1 0.60 1 0.37 0.67 3.46 1 2.05 2 异常 0.67 2.00 3 300 1 0.60 1 0.37 0.33 2.64 0.8 1.99 3 弱异常 0.33 1.83 4 400 1 0.60 1 0.37 0 0.69 0.6 1.91 4 背景 −0.37 −0.37 5 500 0.8 0.58 0.86 0.36 0 0.69 0.4 1.78 5 − − − 6 600 0.6 0.55 0.71 0.36 − − 0.2 1.56 6 − − − 7 700 0.4 0.38 0.57 0.35 − − 0 1.03 7 − − − 8 800 0.2 −0.03 0.43 0.34 − − 0 1.03 8 − − − 9 900 0 −0.03 0.29 0.32 − − − − 9 − − − 10 1000 0 − 0.14 0.30 − − − − 10 − − − 11 1100 − − 0 0.26 − − − − 11 − − − 12 1200 − − 0 0.26 − − − − 12 − − − 13 1300 − − 0 0.26 − − − − 13 − − − 注：“−”为空值。
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