基于大数据的深部找矿靶区定量成矿预测——以大桥地区金矿为例
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王怀涛,王晓伟,罗云之,宋秉田,罗建民,徐磊
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Quantitative Metallogenic Prediction of Deep Prospecting Target Based on Big Data:Taking Gold Deposit in Daqiao Area as an Example
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Huaitao WANG,Xiaowei WANG,Yunzhi LUO,Bingtian SONG,Jianmin LUO,Lei XU
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表1 研究区大桥式金矿定量成矿预测系列模型
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Table 1 Series models of quantitative metallogenic prediction of Daqiao-type gold deposit in the study area
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模型 分类 | 参数 | 变化 系数 | 贡献 | 累计 贡献 | 有效性检验 |
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模型1.1 | (常量) | -1.437 | | | R0=1.62 F0.01=2.32 Fp=30.18 有矿正判率:98.8% 无矿正判率:92% | ΔT1 | 1.68 | 26.37 | 26.37 | ΔT2 | 0.02 | 22.25 | 48.61 | ΔT3 | -0.01 | 17.86 | 66.47 | ΔT4 | 1.19 | 15.7 | 82.17 | ΔT5 | 1.12 | 14.63 | 96.8 | ΔT6 | -1.63 | 1.58 | 98.38 | ΔT7 | -0.01 | 0.93 | 99.31 | ΔT8 | 1.35 | 0.21 | 99.91 | ΔT9 | -0.78 | 0.09 | 100 | 模型1.2 | (常量) | -10.659 | | | R0=0.66 F0.01=3.02 Fp=-14.54 有矿正判率:95.5% 无矿正判率:94.2% | ΔT9 | 10.49 | 57.81 | 57.81 | ΔT10 | -5.47 | 19.56 | 77.36 | ΔT11 | 5.34 | 17.04 | 94.4 | ΔT12 | 0 | 2.88 | 97.28 | ΔT6 | 1.58 | 2.72 | 100 | 模型2.1 | (常量) | 21.295 | | | | ΔT13 | 305.17 | 44.71 | 44.71 | ΔT14 | -178.95 | 26.2 | 70.91 | ΔT15 | -57.81 | 7.8 | 78.71 | R0=0.70 F0.01=2.32 Fp=-11.58 有矿正判率:97.1% 无矿正判率:80.5% | ΔT16 | 0.01 | 7.49 | 86.21 | ΔT17 | -47.62 | 6.28 | 92.48 | ΔT18 | -1.46 | 4.13 | 96.61 | ΔT19 | -24.35 | 1.2 | 97.81 | ΔT4 | 0.81 | 1.1 | 98.91 | ΔT20 | 24.58 | 0.71 | 99.62 | ΔT21 | 3.68 | 0.38 | 100 | 模型2.2 | (常量) | -4.179 | | | R0=0.69 F0.01=1.47 Fp=0.93 有矿正判率:95% 无矿正判率:76.6% | ΔT12 | -0.05 | 31.37 | 31.37 | ΔT3 | 0.04 | 26.6 | 57.98 | ΔT7 | 0.14 | 4.83 | 62.81 | ΔT11 | 56.23 | 4.49 | 67.3 | ΔT22 | -0.13 | 3.17 | 70.47 | ΔT18 | -0.67 | 2.38 | 72.85 | ΔT23 | 5 116.34 | 2.35 | 75.2 | ΔT24 | -42.07 | 1.89 | 77.09 | ΔT25 | -57.96 | 1.69 | 78.78 | ΔT26 | -0.04 | 1.53 | 80.31 |
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