潜在矿产资源评价方法及应用
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摘要
矿产资源短缺、勘查成本和勘查难度剧增的新形势下,如何充分利用海量地质空间数据和现代信息技术,开展快速、高效的潜在矿产资源评价方法及应用研究,对于掌握区域矿产资源潜力,进行矿产勘查部署和选择具体的找矿靶区,从而降低矿产勘查的成本,具有重要的科学意义和实际应用价值。
本论文结合前人研究成果,通过对青海东昆仑成矿带的区域构造特征、区域岩浆岩特征、区域地层特征、成矿地质特征、时空演化特征以及地球物理特征的分析和深入探讨,将青海东昆仑成矿带划分为五个Ⅳ级成矿带:昆北成矿带、昆中成矿带、昆南成矿带、都兰-鄂拉山成矿带和阿尼玛卿成矿带。通过对青海东昆仑成矿带成矿系列和典型矿床组合的深入分析,建立了矿床找矿模型,并建立了青海东昆仑成矿带金属矿床成矿系列,将金属矿床划分为两大矿床组合、7个成矿系列和14个矿床式。
以数据驱动模型(证据权模型、扩展证据权模型、逻辑斯蒂回归模型)为理论基础,结合实际应用需求,开展青海东昆仑地区潜在矿产资源评价。基于数据驱动模型的矿产资源评价技术流程包括:图层的选取依据、图层的重要性评估、图层相关性处理、计算参数检验以及靶区定量化评价。本研究应用证据权模型中的邻近度分析定量刻画了线性控矿因素(断裂、破碎带、褶皱、接触带、蚀变带等)和离散数据(物探数据、化探数据等)与已知矿点之间的空间相关性,用卡方检验、Kolmogorov-Smirnov检验或NOT检验对证据图层之间的相关性进行处理,去除相关性较大的图层,避免过大圈定靶区。应用逻辑斯蒂回归模型进行潜在矿产资源评价时,是在证据权模型中邻近度分析的基础之上,选取最佳的二值证据图层,然后用逻辑斯蒂回归参数(如回归系数、Wald s统计量、回归系数的标准误等)对控矿因素的重要性排序和评价,进而应用非线性回归概率绘制潜在矿产资源预测图。
以区域地质成矿特征和矿床成矿系列和典型矿床模型为理论指导,综合分析不同矿床类型下的控矿因素,建立找矿标志模型。综合多源地质空间数据(基础地质数据、地球物理数据、地球化学数据、遥感数据、已知勘探的矿床/点数据等),应用证据权模型、扩展证据权模型和逻辑斯蒂回归模型分别对青海东昆仑成矿带、野马泉成矿亚带、五龙沟成矿区三个不同空间尺度下的不同矿种开展潜在矿产资源评价。具体应用工作包括:基于证据权模型的青海东昆仑成矿带铜铅锌多金属矿、铁矿和金矿资源评价,基于证据权模型的野马泉成矿亚带铁多金属矿资源评价,基于证据权模型的五龙沟成矿区金矿资源评价;基于扩展证据权模型的青海东昆仑成矿带潜在铜铅锌多金属矿资源评价,基于扩展证据权模型的五龙沟成矿区金矿资源评价;基于逻辑斯蒂回归模型的青海东昆仑成矿带铜铅锌多金属矿和铁矿资源评价,基于逻辑斯蒂回归模型的野马泉成矿亚带铁多金属矿资源评价。
各评价模型(证据权模型、扩展证据权模型、逻辑斯蒂回归模型)的实验结果具体如下:(1)基于证据权模型的青海东昆仑成矿带铁矿资源评价结果显示77%的已知矿点落入中高潜力区,其中高潜力区(总面积的11%)包含了56%的已知矿点,中潜力区(总面积的10%)包含了21%的已知矿点;青海东昆仑成矿带金矿资源评价结果显示74.5%的已知矿点落入中高潜力区,其中高潜力区(总面积的9%)包含了48.9%的已知矿点,中潜力区(总面积的11%)包含25.6%的已知矿点;青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示71.2%的已知矿点落入中高潜力区,其中高潜力区(总面积的8%)包含了31.8%的已知矿点,预测率为47.5%;中潜力区(总面积的19%)包含了39.4%的已知矿点,预测率为25%;野马泉成矿亚带铁多金属矿资源评价结果显示85.7%的已知矿点落入中高潜力区,其中高潜力区(总面积的3.4%)包含了61.9%的已知矿点,中潜力区(总面积的6.2%)包含了23.8%的已知矿点;五龙沟成矿区金矿资源评价结果显示76.5%的已知矿点落入中高潜力区,其中高潜力区(总面积的5.9%)包含70.6%已知矿点,中潜力区(总面积的7.2%)包含了5.9%的已知矿点。(2)基于扩展证据权模型的青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示84.9%的已知点落入中高潜力区,其中高潜力区(总面积的10%)包含了51.9%的已知矿点,中潜力区(总面积的15%)包含了33%的已知矿点;五龙沟成矿区金矿资源评价结果显示94.1%的已知矿点落入中高潜力区,其中高潜力区(总面积的2.6%)包含了76.5%的已知矿点,中潜力区(总面积的3.6%)包含了17.6%的已知矿点。(3)基于逻辑斯蒂回归模型的青海东昆仑成矿带铁多金属矿资源评价结果显示71.6%的已知矿点落入中高潜力区,其中高潜力区(总面积的8.3%)包含43.2%的已知矿点,中潜力区(总面积的15.9%)包含了28.4%的已知矿点;青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示69%的已知矿点落入中高潜力区,其中高潜力区(总面积的8.5%)包含38%的已知矿点,中潜力区(总面积的16.4%)包含了31%的已知矿点;野马泉成矿亚带铁多金属矿资源评价结果显示85.7%的已知矿点落入中高潜力区,其中高潜力区(总面积的4.3%)包含76.2%已知矿点,中潜力区(总面积的5.2%)包含9.5%的已知矿点。不同预测模型的精度表明,在大区域尺度下,扩展证据权模型的较证据权模型和逻辑斯蒂回归模型高,而证据权模型高于逻辑斯蒂回归模型。随着研究区范围减小和研究程度提高,三个评价模型之间的预测精度的差别趋于减小。
With the sharp increasing of the Shortage of mineral resources, prospecting costs and exploration difficulty in the new situation, how to combine the massive geological spatial data with modern information technology efficiently for the study of mineral potential resource evaluation is favorable to mastering regional mineral resource potential, prospecting deployment and selecting specific target, thereby reducing the cost of mineral exploration, which has an important scientific significance and practical value.
According to tectonic features, regional magmatic characteristics, regional stratigraphy, geological characteristics, Spatio-temporal evolution characteristics and geophysical characteristics, the East Kunlun metallogenic belt can be divided into five secondary metallogenic belt:Kunbei belt, Kunzhong belt, Kunnan belt, Dulan-Elashan belt and Animaqing belt. Through analysing and summarizing typical deposits, the metallic deposits within the East Kunlun metallogenic belt can be divided into two types of deposit assemblage, 7 minerogenetic series and 14-type deposits.
Based on the theory of data-driven models, such as weights of evidence model, extended weights of evidence model, and logistic regression model, integrating with practical applications, mineral potential evaluation was carried out in the East Kulun region, Qinghai province. The technical processes of the data-driven model including: evaluating the importance of the layers, processing correlation among layers, calculating test’s parameters and target’s quantitative evaluation. In this paper, weights of evidence model was used to quantitatively characterize the spatial correlation between ore-controlling factors of linear (faults, fracture zones, folds, the contact zone, alteration, etc.) or discrete data (geophysical data, geochemical data, etc.) and discovered deposits/occurrences. The methods used are chi-square test, Kolmogorov-Smirnov test, or "NOT" test, which can remove high correlation among map layers, and avoid excessive delineating targets. Using the Logistic regression model for mineral potential evaluation, the optimal binary evidential maps were derived from proximity analysis of weights of evidence model, then logistic regression parameters (such as the regression coefficient Wald's statistic, standard error of regression coefficients, etc.) were calculated to rank ore-controlling factors based on the importance of some parameters, last mapping mineral potential according to the nonlinear regression probability.
Regional metallogenic characteristics, minerogenetic series and typical deposit model as theoretical guidances, combining with multi-source geological data, such as geophysical data, geochemical data, remote sensing data with discovered deposits /occurrences, comprehensive analysis of different deposit types under the ore-con- trolling factors, constructing prospecting model. The weight of evidence model, the extended weight of evidence model, and the logistic regression model were used to map mineral potential for three study areas, the East Kunlun metallogenic belt, the Yemaquan secondary metallogenic belt and the Wulonggou metallogenic district with different spatial scales. The work has been done as follows: mineral resource evaluation in the East Kunlun region belt based on weights of evidence model for copper-lead-zinc polymetallic resources, gold resources and iron resources, mineral resource evaluation for iron polymetallic resources in the Yemaquan secondary metallogenic belt based on weights of evidence model, mineral resource evaluation for gold resources in the Wulonggou metallogenic district based on weights of evidence model; mineral resource evaluation for copper-lead-zinc polymetallic resources in the East Kunlun region belt based on extended weights of evidence model, mineral resource evaluation for gold resources in the Wulonggou metallogenic district based on extended weights-of-evidence model; mineral resource evaluation in the East Kunlun region belt based on logistic regression model for copper-lead-zinc polymetallic resources and iron resources, mineral resource evaluation for iron polymetallic resources in the Yemaquan secondary metallogenic belt based on logistic regression model.
The experimental results of the evaluation models (weights of evidence model, extended weights of evidence model, logistic regression model) indicate that: (1) The results derived from the evaluation of iron resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 77% of the total deposits, among which the high potential area occupies 11% of the total area, containing 56% deposits, and the moderate potential area occupies 10%, containing 21% deposits.The results derived from the evaluation of gold resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 74.5% of the total deposits, among which the high potential area occupies 9% of the total area, containing 48.9% deposits, and the moderate potential area occupies 11%, containing 25.6% deposits.The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 71.2% of the total deposits, among which the high potential area occupies 8% of the total area, containing 31.8% deposits, and predicting 47.5% deposits, the moderate potential area occupies 19%, containing 39.4% deposits, and predicting 25% deposits.The results derived from the evaluation of iron polymetallic resources in the Yemaquan secondary metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 85.7% of the total deposits, among which the high potential area occupies 3.4% of the total area, containing 61.9% deposits, and the moderate potential area occupies 6.2%, containing 23.8% deposits. The results derived from the evaluation of gold resources in the Wulonggou metallogenic district based on weights of evidence model indicate that high and moderate potential area contains 76.5% of the total deposits, among which the high potential area occupies 5.9% of the total area, containing 70.6% deposits, and the moderate potential area occupies 7.2%, containing 5.9% deposits. (2) The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on extended weights of evidence model indicate that high and moderate potential area contains 84.9% of the total deposits, among which the high potential area occupies 10% of the total area, containing 51.9% deposits, and the moderate potential area occupies 15%, containing33% deposits. The results derived from the evaluation of gold resources in the Wulonggou metallogenic district based on extended weights of evidence model indicate that high and moderate potential area contains 94.1% of the total deposits, among which the high potential area occupies 2.6% of the total area, containing 76.5% deposits, and the moderate potential area occupies 3.6%, containing 17.6% deposits. (3) The results derived from the evaluation of iron resources in the East Kunlun metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 71.6% of the total deposits, among which the high potential area occupies 8.3% of the total area, containing 43.2% deposits, and the moderate potential area occupies 15.9%, containing 28.4% deposits. The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 69% of the total deposits, among which the high potential area occupies 8.5% of the total area, containing 38% deposits, and the moderate potential area occupies 16.4%, containing 31% deposits. The results derived from the evaluation of iron polymetallic resources in the Yemaquan secondary metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 85.7% of the total deposits, among which the high potential area occupies 4.3% of the total area, containing 76.2% deposits, and the moderate potential area occupies 5.2%, containing 9.5%
deposits. The prediction accuracy of different data-driven models indicate that the extended weight of evidence model is higher than the weight of evidence model and the logistic regression model, whereas the weight of evidence model is higher than the logistic regression model at a large regional scale. With the scope decreasing and the research increasing of the study area, the difference of the prediction accuracy among data-driven models tends to decrease.
本论文结合前人研究成果,通过对青海东昆仑成矿带的区域构造特征、区域岩浆岩特征、区域地层特征、成矿地质特征、时空演化特征以及地球物理特征的分析和深入探讨,将青海东昆仑成矿带划分为五个Ⅳ级成矿带:昆北成矿带、昆中成矿带、昆南成矿带、都兰-鄂拉山成矿带和阿尼玛卿成矿带。通过对青海东昆仑成矿带成矿系列和典型矿床组合的深入分析,建立了矿床找矿模型,并建立了青海东昆仑成矿带金属矿床成矿系列,将金属矿床划分为两大矿床组合、7个成矿系列和14个矿床式。
以数据驱动模型(证据权模型、扩展证据权模型、逻辑斯蒂回归模型)为理论基础,结合实际应用需求,开展青海东昆仑地区潜在矿产资源评价。基于数据驱动模型的矿产资源评价技术流程包括:图层的选取依据、图层的重要性评估、图层相关性处理、计算参数检验以及靶区定量化评价。本研究应用证据权模型中的邻近度分析定量刻画了线性控矿因素(断裂、破碎带、褶皱、接触带、蚀变带等)和离散数据(物探数据、化探数据等)与已知矿点之间的空间相关性,用卡方检验、Kolmogorov-Smirnov检验或NOT检验对证据图层之间的相关性进行处理,去除相关性较大的图层,避免过大圈定靶区。应用逻辑斯蒂回归模型进行潜在矿产资源评价时,是在证据权模型中邻近度分析的基础之上,选取最佳的二值证据图层,然后用逻辑斯蒂回归参数(如回归系数、Wald s统计量、回归系数的标准误等)对控矿因素的重要性排序和评价,进而应用非线性回归概率绘制潜在矿产资源预测图。
以区域地质成矿特征和矿床成矿系列和典型矿床模型为理论指导,综合分析不同矿床类型下的控矿因素,建立找矿标志模型。综合多源地质空间数据(基础地质数据、地球物理数据、地球化学数据、遥感数据、已知勘探的矿床/点数据等),应用证据权模型、扩展证据权模型和逻辑斯蒂回归模型分别对青海东昆仑成矿带、野马泉成矿亚带、五龙沟成矿区三个不同空间尺度下的不同矿种开展潜在矿产资源评价。具体应用工作包括:基于证据权模型的青海东昆仑成矿带铜铅锌多金属矿、铁矿和金矿资源评价,基于证据权模型的野马泉成矿亚带铁多金属矿资源评价,基于证据权模型的五龙沟成矿区金矿资源评价;基于扩展证据权模型的青海东昆仑成矿带潜在铜铅锌多金属矿资源评价,基于扩展证据权模型的五龙沟成矿区金矿资源评价;基于逻辑斯蒂回归模型的青海东昆仑成矿带铜铅锌多金属矿和铁矿资源评价,基于逻辑斯蒂回归模型的野马泉成矿亚带铁多金属矿资源评价。
各评价模型(证据权模型、扩展证据权模型、逻辑斯蒂回归模型)的实验结果具体如下:(1)基于证据权模型的青海东昆仑成矿带铁矿资源评价结果显示77%的已知矿点落入中高潜力区,其中高潜力区(总面积的11%)包含了56%的已知矿点,中潜力区(总面积的10%)包含了21%的已知矿点;青海东昆仑成矿带金矿资源评价结果显示74.5%的已知矿点落入中高潜力区,其中高潜力区(总面积的9%)包含了48.9%的已知矿点,中潜力区(总面积的11%)包含25.6%的已知矿点;青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示71.2%的已知矿点落入中高潜力区,其中高潜力区(总面积的8%)包含了31.8%的已知矿点,预测率为47.5%;中潜力区(总面积的19%)包含了39.4%的已知矿点,预测率为25%;野马泉成矿亚带铁多金属矿资源评价结果显示85.7%的已知矿点落入中高潜力区,其中高潜力区(总面积的3.4%)包含了61.9%的已知矿点,中潜力区(总面积的6.2%)包含了23.8%的已知矿点;五龙沟成矿区金矿资源评价结果显示76.5%的已知矿点落入中高潜力区,其中高潜力区(总面积的5.9%)包含70.6%已知矿点,中潜力区(总面积的7.2%)包含了5.9%的已知矿点。(2)基于扩展证据权模型的青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示84.9%的已知点落入中高潜力区,其中高潜力区(总面积的10%)包含了51.9%的已知矿点,中潜力区(总面积的15%)包含了33%的已知矿点;五龙沟成矿区金矿资源评价结果显示94.1%的已知矿点落入中高潜力区,其中高潜力区(总面积的2.6%)包含了76.5%的已知矿点,中潜力区(总面积的3.6%)包含了17.6%的已知矿点。(3)基于逻辑斯蒂回归模型的青海东昆仑成矿带铁多金属矿资源评价结果显示71.6%的已知矿点落入中高潜力区,其中高潜力区(总面积的8.3%)包含43.2%的已知矿点,中潜力区(总面积的15.9%)包含了28.4%的已知矿点;青海东昆仑成矿带铜铅锌多金属矿资源评价结果显示69%的已知矿点落入中高潜力区,其中高潜力区(总面积的8.5%)包含38%的已知矿点,中潜力区(总面积的16.4%)包含了31%的已知矿点;野马泉成矿亚带铁多金属矿资源评价结果显示85.7%的已知矿点落入中高潜力区,其中高潜力区(总面积的4.3%)包含76.2%已知矿点,中潜力区(总面积的5.2%)包含9.5%的已知矿点。不同预测模型的精度表明,在大区域尺度下,扩展证据权模型的较证据权模型和逻辑斯蒂回归模型高,而证据权模型高于逻辑斯蒂回归模型。随着研究区范围减小和研究程度提高,三个评价模型之间的预测精度的差别趋于减小。
With the sharp increasing of the Shortage of mineral resources, prospecting costs and exploration difficulty in the new situation, how to combine the massive geological spatial data with modern information technology efficiently for the study of mineral potential resource evaluation is favorable to mastering regional mineral resource potential, prospecting deployment and selecting specific target, thereby reducing the cost of mineral exploration, which has an important scientific significance and practical value.
According to tectonic features, regional magmatic characteristics, regional stratigraphy, geological characteristics, Spatio-temporal evolution characteristics and geophysical characteristics, the East Kunlun metallogenic belt can be divided into five secondary metallogenic belt:Kunbei belt, Kunzhong belt, Kunnan belt, Dulan-Elashan belt and Animaqing belt. Through analysing and summarizing typical deposits, the metallic deposits within the East Kunlun metallogenic belt can be divided into two types of deposit assemblage, 7 minerogenetic series and 14-type deposits.
Based on the theory of data-driven models, such as weights of evidence model, extended weights of evidence model, and logistic regression model, integrating with practical applications, mineral potential evaluation was carried out in the East Kulun region, Qinghai province. The technical processes of the data-driven model including: evaluating the importance of the layers, processing correlation among layers, calculating test’s parameters and target’s quantitative evaluation. In this paper, weights of evidence model was used to quantitatively characterize the spatial correlation between ore-controlling factors of linear (faults, fracture zones, folds, the contact zone, alteration, etc.) or discrete data (geophysical data, geochemical data, etc.) and discovered deposits/occurrences. The methods used are chi-square test, Kolmogorov-Smirnov test, or "NOT" test, which can remove high correlation among map layers, and avoid excessive delineating targets. Using the Logistic regression model for mineral potential evaluation, the optimal binary evidential maps were derived from proximity analysis of weights of evidence model, then logistic regression parameters (such as the regression coefficient Wald's statistic, standard error of regression coefficients, etc.) were calculated to rank ore-controlling factors based on the importance of some parameters, last mapping mineral potential according to the nonlinear regression probability.
Regional metallogenic characteristics, minerogenetic series and typical deposit model as theoretical guidances, combining with multi-source geological data, such as geophysical data, geochemical data, remote sensing data with discovered deposits /occurrences, comprehensive analysis of different deposit types under the ore-con- trolling factors, constructing prospecting model. The weight of evidence model, the extended weight of evidence model, and the logistic regression model were used to map mineral potential for three study areas, the East Kunlun metallogenic belt, the Yemaquan secondary metallogenic belt and the Wulonggou metallogenic district with different spatial scales. The work has been done as follows: mineral resource evaluation in the East Kunlun region belt based on weights of evidence model for copper-lead-zinc polymetallic resources, gold resources and iron resources, mineral resource evaluation for iron polymetallic resources in the Yemaquan secondary metallogenic belt based on weights of evidence model, mineral resource evaluation for gold resources in the Wulonggou metallogenic district based on weights of evidence model; mineral resource evaluation for copper-lead-zinc polymetallic resources in the East Kunlun region belt based on extended weights of evidence model, mineral resource evaluation for gold resources in the Wulonggou metallogenic district based on extended weights-of-evidence model; mineral resource evaluation in the East Kunlun region belt based on logistic regression model for copper-lead-zinc polymetallic resources and iron resources, mineral resource evaluation for iron polymetallic resources in the Yemaquan secondary metallogenic belt based on logistic regression model.
The experimental results of the evaluation models (weights of evidence model, extended weights of evidence model, logistic regression model) indicate that: (1) The results derived from the evaluation of iron resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 77% of the total deposits, among which the high potential area occupies 11% of the total area, containing 56% deposits, and the moderate potential area occupies 10%, containing 21% deposits.The results derived from the evaluation of gold resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 74.5% of the total deposits, among which the high potential area occupies 9% of the total area, containing 48.9% deposits, and the moderate potential area occupies 11%, containing 25.6% deposits.The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 71.2% of the total deposits, among which the high potential area occupies 8% of the total area, containing 31.8% deposits, and predicting 47.5% deposits, the moderate potential area occupies 19%, containing 39.4% deposits, and predicting 25% deposits.The results derived from the evaluation of iron polymetallic resources in the Yemaquan secondary metallogenic belt based on weights of evidence model indicate that high and moderate potential area contains 85.7% of the total deposits, among which the high potential area occupies 3.4% of the total area, containing 61.9% deposits, and the moderate potential area occupies 6.2%, containing 23.8% deposits. The results derived from the evaluation of gold resources in the Wulonggou metallogenic district based on weights of evidence model indicate that high and moderate potential area contains 76.5% of the total deposits, among which the high potential area occupies 5.9% of the total area, containing 70.6% deposits, and the moderate potential area occupies 7.2%, containing 5.9% deposits. (2) The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on extended weights of evidence model indicate that high and moderate potential area contains 84.9% of the total deposits, among which the high potential area occupies 10% of the total area, containing 51.9% deposits, and the moderate potential area occupies 15%, containing33% deposits. The results derived from the evaluation of gold resources in the Wulonggou metallogenic district based on extended weights of evidence model indicate that high and moderate potential area contains 94.1% of the total deposits, among which the high potential area occupies 2.6% of the total area, containing 76.5% deposits, and the moderate potential area occupies 3.6%, containing 17.6% deposits. (3) The results derived from the evaluation of iron resources in the East Kunlun metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 71.6% of the total deposits, among which the high potential area occupies 8.3% of the total area, containing 43.2% deposits, and the moderate potential area occupies 15.9%, containing 28.4% deposits. The results derived from the evaluation of copper-lead-zinc polymetallic resources in the East Kunlun metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 69% of the total deposits, among which the high potential area occupies 8.5% of the total area, containing 38% deposits, and the moderate potential area occupies 16.4%, containing 31% deposits. The results derived from the evaluation of iron polymetallic resources in the Yemaquan secondary metallogenic belt based on logistic regression model indicate that high and moderate potential area contains 85.7% of the total deposits, among which the high potential area occupies 4.3% of the total area, containing 76.2% deposits, and the moderate potential area occupies 5.2%, containing 9.5%
deposits. The prediction accuracy of different data-driven models indicate that the extended weight of evidence model is higher than the weight of evidence model and the logistic regression model, whereas the weight of evidence model is higher than the logistic regression model at a large regional scale. With the scope decreasing and the research increasing of the study area, the difference of the prediction accuracy among data-driven models tends to decrease.
引文
[1]阿成业,王毅智,任晋祁,等.东昆仑地区万保沟群的解体及早寒武世地层的新发现[J].中国地质, 2003, 30(2): 199–206.
[2]边千韬,赵大升,叶正仁,等.初论昆祁秦缝合系[J].地球学报, 2002, 23(6): 501–508.
[3]程裕琪,陈毓川,赵一鸣.初论矿床的成矿系列问题[J].中国地质科学院院报, 1979, 1(1): 32–58.
[4]程裕淇.再论矿床的成矿系列问题[J].中国地质科学院院报, 1983, 1–64.
[5]陈毓川.当代矿产资源勘查评价的理论与方法[M] .北京:地震出版社. 1999. 1-549.
[6]陈毓川,裴荣富,宋天锐,等.中国矿床成矿系列初论[M].北京:地质出版社, 1998, 1–121.
[7]陈毓川等.中国成矿体系与区域成矿评价[M].北京:地质出版社, 2007, 1-1004.
[8]陈能松,孙敏,王勤燕,等.东昆仑造山带中带的锆石U-Pb定年与构造演化启示[J].中国科学D辑:地球科学, 2008, 38(6): 657–666.
[9]陈能松,何蕾,孙敏,等.东昆仑造山带早古生代变质峰期和逆冲构造变形年代的精确限定[J].科学通报, 2002, 47(8): 628–631.
[10]陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社, 1999, 1–240.
[11]谌宏伟,罗照华,莫宣学,等.东昆仑造山带三叠纪岩浆混合成因花岗岩的岩浆底侵作用机制[J].中国地质, 2005, 32(3): 385–395.
[12]陈守建,李荣社,计文化,等.昆仑造山带二叠纪岩相古地理特征及盆山转换探讨[J].中国地质,2010, 37(2): 374–393.
[13]陈守建,李荣社,计文化,等.昆仑造山带石炭纪岩相特征及构造古地理[J].地球科学与环境学报, 2008, 30(3): 221–233.
[14]成秋明.非线性成矿预测理论:多重分形奇异性–广义自相似性-分形谱系模型与方法[J].地球科学–中国地质大学学报, 2006, 31(3): 337–348.
[15]成秋明.地质异常的奇异性度量与隐伏源致矿异常识别[J].地球科学–中国地质大学学报, 2011, 36(2): 307-316.
[16]成秋明.成矿过程奇异性与矿产预测定量化的新理论与新方法[J].地学前缘, 2007, 14(5): 42–53.
[17]段吉业,葛肖虹.中国西北地区各构造单元之间地层和生物古地理的亲缘关系-兼论西北地区构造格局[J].地质通报, 2005, 24(6): 558–563.
[18]党兴彦,范桂忠,李智明,等.东昆仑成矿带典型矿床分析[J].西北地质, 2006, 39(2): 143–155.
[19]党兴彦,李智明.青海昆仑山口成矿区矿产资源潜力评价[R].青海省地质调查院, 2004.
[20]丁正江,孙丰月,李碧乐.青海省苦海汞(金)矿床地质特征及找矿前景分析[J].地质与勘探, 2010, 46(2): 198–206.
[21]董树文,项怀顺,高锐,等.长江中下游庐江–枞阳火山岩矿集区深部结构与成矿作用[J].岩石学报, 2010, 26(6): 2529–2542.
[22]丰成友,张德全,党兴彦,等.青海格尔木地区驼路沟钴(金)矿床石英钠长石岩锆石SHRIMPU -Pb定年对―纳赤台群‖时代的制约[J].地质通报, 2005, 24(6): 501–505.
[23]丰成友,张德全,屈文俊,等.青海格尔木驼路沟喷流沉积型钴(金)矿床的黄铁矿Re–Os定年[J].地质学报, 2006, 80(4): 571–576.
[24]丰成友,张德全,王富春,等.青海东昆仑复合造山过程及典型造山型金矿地质[J].地球学报, 2004, 25(4): 415–422.
[25]丰成友,李东生,吴正寿,等.青海东昆仑成矿带斑岩型矿床的确认及找矿前景分析[J].矿物学报,增刊, 2009, p. 171–172.
[26]葛肖虹,刘俊来.被肢解的―西域克拉通‖[J].岩石学报, 2000, 16(1): 59–66.
[27]何书跃,祁兰英,舒树兰,等.青海祁漫塔格地区斑岩铜矿的成矿条件和远景[J].地质与勘探, 2008, 44(2): 14–22.
[28]郭宪璞,王乃文,丁孝忠.东昆仑格尔木南部纳赤台群和万宝沟群基质系统与外来系统地球化学差异[J].地质通报, 2004, 23(12): 1188–1195.
[29]郭宪璞,王乃文,丁孝忠,等.青海东昆仑纳赤台群基质系统与外来系统的关系[J].地质通报, 2003, 22 (3): 160–164.
[30]郭正府,邓晋福,许志琴,等.青藏东昆仑晚古生代末–中生代中酸性火成岩与陆内造山过程[J].现代地质, 1998, 12(3): 344–352.
[31]郭晓东,张玉杰,刘桂阁,等.东昆仑地区金铜等成矿规律及找矿方向[J].黄金地质, 2004, 10(4): 16–22.
[32]高章鉴,罗才让,井继锋.青海省肯德可克金矿热水沉积层矽卡岩特征及成矿意义[J].西北地质, 2001, 34(2): 50–53.
[33]高晓峰,校培喜,谢从瑞,等.东昆仑阿牙克库木湖北巴什尔希花岗岩锆石LA-ICP-MS U-Pb定年及其地质意义[J].地质学报, 2010, 29(7): 1001–1008.
[34]高锐,卢占武,刘金凯,等.庐–枞金属矿集区深地震反射剖面解释结果–揭露地壳精细结构,追踪成矿深部过程[J].岩石学报, 2010, 26(9): 2543–2552.
[35]郝杰,刘小汉,桑海清.新疆东昆仑阿牙克岩体地球化学与40Ar–39Ar年代学研究及其大地构造意义[J].岩石学报, 2003, 19(3): 517–522.
[36]侯增谦.大陆碰撞成矿论[J].地质学报, 2010, 84(1): 30–58.
[37]姜春发,杨经绥,冯秉贵,等.昆仑开合构造[M].北京:地质出版社, 1992, 1–224.
[38]姜耀辉,芮行健,贺菊瑞,等.西昆仑加里东期花岗岩类构造的类型及大地构造意义[J].岩石学报, 1999, 15(1): 105–115.
[39]寇玉才,李战业,王英孝.尕林格矽卡岩型铁多金属矿床地质–地球物理模型[J].西北地质, 2010, 43(2): 20–31.
[40]李宏录,卫岗,曾宪刚,等.应用航磁资料在野马泉地区寻找以铁为主多金属矿产[J].物探与化探, 2009, 33(2): 117-122.
[41]李厚民,沈远超,胡正国,等.青海五龙沟金矿床矿石、矿物含金性及金的赋存状态[J].矿物学报, 2001, 21(1): 89–94.
[42]李世金,孙丰月,丰成友,等.青海东昆仑鸭子沟多金属矿的成矿年代学研究[J].地质学报, 2008a, 82(7): 949–955.
[43]李世金,孙丰月,王力,等.青海东昆仑卡尔却卡多金属矿区斑岩型铜矿的流体包裹体研究[J].矿床地质, 2008b, 27(3): 399–406.
[44]李东生,奎明娟,古凤宝,等.青海赛什塘铜矿床的地质特征及成因探讨[J].地质学报, 2009, 83(5): 719–730.
[45]李荣社,计文化,赵振明,等.昆仑早古生代造山带研究进展[J].地质通报, 2007,26 (4): 373–381.
[46]李光岑,林宝玉.昆仑山东段几个地质问题的探讨[C].青藏高原地质论文集. 1982, 28–48.
[47]李廷栋,肖序常.青藏高原地体构造分析-青藏高原岩石圈结构构造和形成演化[C].中华人民共和国地质矿产部地质专报, 1996, 20: 6–20.
[48]李智明,薛春纪,王晓虎,等.东昆仑区域成矿特征及有关找矿突破问题分析[J].地学评论, 2007, 53(5): 708–718.
[49]李裕伟.空间信息技术的发展及其在地球科学中的应用[J].地学前缘, 1998, 5(1-2): 335–341.
[50]李裕伟,赵精满,李晨阳. 2007.基于GMS/DSS和GIS的潜在矿产资源评价方法[M].地震出版社, 1-946.
[51]刘继庆,胡正国,钱壮志,等.东昆仑NW向线性构造带地质特征及找矿意义[J].西安工程学院学报, 2000, 22(2): 18–21.
[52]刘凤山,石准立.太行山燕山造山带与中生代花岗岩有关的金属矿床成矿系列特征[J].矿床地质, 1998, 17(3): 193–203.
[53]刘云华,莫宣学,张雪亭,等.东昆仑野马泉地区矽卡岩矿床地质特征及控矿条件[J].华南地质与矿产, 2005, 3: 18–23.
[54]刘云华,莫宣学,喻学惠,等.东昆仑野马泉地区景忍花岗岩锆石SHRIMP U-Pb定年及其地质意义[J].岩石学报, 2006, 22(10): 2457–2463.
[55]刘云华.东昆仑祁漫塔格地区多金属成矿作用及其与花岗岩的关系[D].北京:中国地质大学(北京),博士论文,2004,1–146.
[56]刘增铁,任家琪,邬介人,等.青海铜矿[M].北京:地质出版社, 2008, 1-291.
[57]刘成东,莫言学,罗照华,等.东昆仑壳–幔岩浆混合作用:来自锆石SHRIMP年代学的证据[J].科学通报, 2004, 49(6): 592–602.
[58]刘战庆,裴先治,李瑞保,等.东昆仑南缘阿尼玛卿构造带布青山地区两期蛇绿岩的LA-ICP-MS锆石U-Pb定年及其构造意义[J].地质学报, 2011, 85(2): 185-194.
[59]罗照华,柯珊,曹永清,等.东昆仑印支晚期幔源岩浆活动[J].地质通报, 2002, 21 (6): 292–297.
[60]罗照华,邓晋福,曹永清,等.青海省东昆仑地区晚古生代–早中生代火山活动与区域构造演化[J].现代地质, 1999, 13(1): 51–56.
[61]吕庆田,廉玉广,赵金花.反射地震技术在成矿地质背景与深部矿产勘查中的应用:现状与前景[J].地质学报, 2010a, 84(6): 771–787.
[62]吕庆田,韩立国,严加冰,等.庐枞矿集区火山气液型铁、硫矿床及控矿构造的反射地震成像[J].岩石学报, 2010b, 26(6): 2598–2612.
[63]吕庆田,侯增谦,史大年,等.铜陵狮子山金属矿地震反射结果及对区域成矿的意义[J].地质学报, 2004, 23(3): 390–398.
[64]梅燕雄,裴荣富,李进文,等.中国中生代矿床成矿系列类型及其演化[J].矿床地质, 2004, 23(2): 190–197.
[65]莫宣学,邓晋福.东昆仑中段铜金成矿条件及找矿方向的框架研究[R].北京:中国地质大学, 1998.
[66]潘桂堂,陈智梁,李兴振,等.东特提斯地质构造形成演化[M].北京:地质出版社, 1997.
[67]潘彤,马梅生,康祥瑞.东昆仑肯德可克及外围钴多金属矿找矿突破的启示[J].中国地质, 2001, 28(2): 17-20.
[68]潘彤,罗才让,伊有昌,等.青海省金属矿产成矿规律及成矿预测[M].北京:地质出版社, 2006, 1-227.
[69]潘裕生,周伟明,许荣华,等.昆仑山早古生代地质特征与演化[J].中国科学(D辑), 1996, 26(4): 302–607.
[70]钱壮志,胡正国,刘继庆.东昆仑北西向韧性剪切带发育的区域构造背景–以石灰沟韧性剪切带为例[J].成都理工学院学报, 1998, 25(2): 201–205.
[71]钱壮志,胡正国,刘继庆,等.古特提斯东昆仑活动陆缘及其区域成矿[J].大地构造与成矿学, 2000, 24(2): 134–139.
[72]青海省地质矿产局.区域地质调查报告(1:20万),格尔木市幅(J2462[35])、纳赤台幅(I2462[5])[R], 1981, 1–211.
[73]任涛,王瑞廷,王向阳,等.秦岭造山带柞水_山阳沉积盆地铜矿勘查思路与方法[J].地质学报, 2009, 83(11): 1730–1738.
[74]史仁灯,杨经绥,吴才来,等.青藏高原北部柴北缘超高压变质带中的岛弧火山岩:洋–陆俯冲伴随陆–陆俯冲的证据[J].地质学报, 2004, 78: 52–64.
[75]宋治杰,张汉文,李文明,等.青海鄂拉山地区铜多金属矿床的成矿条件及成矿模式[J].西北地质科学, 1999, 16(1): 134–144.
[76]佘宏全,张德全,景向阳,等.青海省乌兰乌珠尔斑岩铜矿床地质特征与成因[J].中国地质, 2007, 34(2): 306-314.
[77]肖克炎,丁建华,娄德波.试论成矿系列与矿产资源评价[J].矿床地质,2009, 28(3): 357–365.
[78]魏启荣,李德威,王国灿.东昆仑万保沟群火山岩(Pt2w)岩石地球化学特征及其构造背景[J].矿物岩石, 2007, 27(1): 97–106.
[79]吴庭祥,李宏录.青海尕林格地区铁多金属矿床的地质特征与地球化学特征[J].矿物岩石地球化学通报, 2009, 28(2): 157–161.
[80]吴健辉,丰成友,张德全,等.柴达木盆地南缘祁漫塔格-鄂拉山地区斑岩–矽卡岩矿床地质[J].矿床地质, 2010, 29(5): 760–774.
[81]王移生.青海日龙沟锡–多金属矿床地质特征及成矿作用[J].西北地质, 1990, 2: 43-48.
[82]王松,丰成友,李世金,等.青海祁漫塔格卡尔却卡铜多金属矿区花岗闪长岩锆石SHRIMPU-Pb测年及其地质意[J].中国地质, 2009, 36(1): 74-84.
[83]王登红,应立娟,王成辉,等.中国贵金属矿床的基本成矿规律与找矿方向[J].地学前缘, 2007, 14(5): 71–81.
[84]王国灿,王青海,简平,等.东昆仑前寒武纪基底变质岩系的锆石SHRIMP年龄及其构造意义[J].地学前缘, 2004, 11(4): 481–490.
[85]王国灿,张天平,梁斌,等.东昆仑造山带东段昆中复合蛇绿混杂岩带及―东昆中断裂带‖地质涵义[J].地球科学–中国地质大学学报, 1999, 24(2): 129–133.
[86]王国灿,向树元,王岸,等.东昆仑及相邻地区中生代-新生代早期构造过程的热年代学记录[J].地球科学-中国地质大学学报, 2007a, 32(5): 605–614.
[87]王国灿,魏启荣,贾春兴,等.关于东昆仑地区前寒武纪地质的几点认识[J].地质通报, 2007b, 26(8): 929–937.
[88]王秉璋,张智勇,张森琦,等.东昆仑东端苦海–赛什塘地区晚古生代蛇绿岩的地质特征[J].地球科学–中国地质大学学报, 2000, 25(6): 592-598.
[89]王惠初,陆松年,袁桂邦,等.柴达木盆地北缘滩间山群的构造属性及形成时代[J].地质通报, 2003, 22(7): 487-493.
[90]王世称,范继璋,杨永华.矿产资源评价[M].吉林:吉林科学技术出版社1990, 1–246
[91]王世称,陈永良,夏立显.综合信息矿产预测理论与方法[M].北京:科学出版社, 2002, 1–343.
[92]夏庆霖,成秋明,左仁广等.基于GIS矿产勘查靶区优选技术[J].中国地质大学–地球科学学报, 2009, 34(2): 287– 293.
[93]许志琴,崔军文,张建新.大陆山链变形构造动力学[M].北京:冶金工业出版社, 1996, 1–246.
[94]许志琴,杨经绥,嵇少丞,等.中国大陆构造及动力学若干问题的认识[J].地质学报, 2010, 84(1): 1–29.
[95]徐文艺,张德全,阎升好,等.东昆仑地区矿产资源大调查进展与前景展望[J].中国地质, 2001, 28(1): 25–29.
[96]尹安.喜马拉雅–青藏高原造山带地质演化–显生宙亚洲大陆生长[J].地球学报, 2001, 22(3): 193– 230.
[97]殷鸿福,张克信.东昆仑造山带的一些特点[J].地球科学–中国地质大学学报, 1997, 22(4): 339– 342.
[98]殷鸿福,张克信.中华人民共和国区域地质调查报告:冬给措纳湖幅( I47C001002),比例尺1∶250,000[R].武汉:中国地质大学出版社, 2003.
[99]杨经绥,许志琴,马昌前,等.复合造山作用和中国中央造山带的科学问题[J].中国地质, 2010, 37(1): 1–11.
[100]杨小斌,杨宝荣,王晓云.青海果洛龙洼金矿床金的赋存状态研究[J].地质与勘探, 2006, 42(5): 57–59.
[101]燕宁,李社宏,陆智平.青海省兴海县索拉沟铜多金属矿成矿地质特征与矿床成因[J].大地构造与成矿学, 2011, 35(1): 161-166.
[102]闫臻,胡正国,刘继庆,等.东昆仑开荒北金矿床地质特征及控矿条件[J].西安工程学院学报, 2000, 22(1): 23–27.
[103]叶天竺,肖克炎,严光.矿床模型综合地质信息预测技术研究[J].地学前缘, 2007, 14(5): 11-19.
[104]袁万明,莫宣学,喻学惠,等,东昆仑热液金成矿带及其找矿方向[J].地质与勘探, 2000, 36(5): 20–23.
[105]袁桂邦,王惠初,李惠民,等.柴北缘绿梁山地区辉长岩的锆石U-Pb年龄及意义[J].前寒武纪研究进展, 2002, 25(1): 36–39.
[106]张爱奎,莫宣学,李云平,等.青海西部祁漫塔格成矿带找矿新进展及其意义[J].地质通报, 2010, 29(7): 1062–1074.
[107]张德全,党兴彦,佘宏全,等.柴北缘–东昆仑地区造山型金矿床的Ar–Ar测年及其地质意义[J].矿床地质, 2005, 24(2): 87–98.
[108]张德全,王富春,佘宏全,等.柴北缘–东昆仑地区造山型金矿床的三级控矿构造系统[J].中国地质, 2007, 34(1): 92–100.
[109]张德全,佘宏全,徐文艺,等.驼路沟喷气沉积型钴(金)矿床成矿地质背景及矿床成因的地球化学限制[J].地球学报, 2002a, 23(6): 527–534.
[110]张德全,徐文艺,贾群子,等.东昆仑地区综合找矿预测与突破[R].北京:中国地质科学院, 2002b.
[111]张德全,丰成友,李大新,等.柴北缘-东昆仑地区的造山型金矿床[J].矿床地质, 2001, 20(2): 137–146.
[112]张汉文.青海铜峪沟铜矿床的矿化特征、环境和矿床类型[J].西北地质, 2001, 34 (4): 30–42.
[113]张建新,孟繁聪,万渝生,等.柴达木盆地南缘金水口群的早古生代构造热事件:锆石U-Pb SHRIMP年龄证据[J].地质通报, 2003, 22(6): 397–404.
[114]张智勇,殷鸿福,王秉璋,等.昆秦接合部海西期苦海–赛什塘分支洋的存在及其证据[J].地球科学–中国地质大学学报, 2004, 29(6): 691–696.
[115]朱云海,陈能松,王国灿,等.东昆中蛇绿岩中单斜辉石、角闪石矿物成分特征及岩石学意义[J].地球科学–中国地质大学学报, 1997, 22(4): 364-368.
[116]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].武汉:中国地质大学出版社, 1983,1–272.
[117]赵鹏大,池顺都.初论地质异常[J].地球科学–中国地质大学学报, 1991, 16(3): 241–248.
[118]赵鹏大.―三联式‖资源定量预测与评价-数字找矿理论与实践探讨[J].地球科学–中国地质大学学报[J], 2002, 27(5): 482–489.
[119]赵鹏大,陈建平,张寿庭.―三联式‖成矿预测新进展[J].地学前缘, 2003, 10(2): 455-463.
[120]赵鹏大,陈永清.科学选靶的理论与途径[J].地球科学–中国地质大学学报, 2011, 36(2): 181– 188.
[121]赵鹏大.成矿定量预测与深部找矿[J].地学前缘, 2007, 14(5): 1–10.
[122]邹长毅,史长义.五龙沟金矿区区域地球化学异常特征及找矿标志[J].中国地质, 2004, 31(4): 420–423.
[123] Agterberg F P. Application of image analysis and multivariate analysis to mineral resource appraisal [J]. Economic Geology, 1981, 76: 1016–1031.
[124] Agterberg F P, Divi S R. A statistical model for the distribution of copper, lead, and zinc in the Canadian Appalachian region [J]. Economic Geology, 1978, 73(2): 230–245.
[125] Agterberg F P, Bonham-Carter G F, Cheng Q, et al. Weights of evidence modeling and weighted logistic regression in mineral potential mapping [C]. In Davis, J. C., and Herzfeld, U. C., eds., Computers in Geology: Oxford Univ. Press, New York, 1993, p. 13–32.
[126] Agterberg F P, Cheng Q. Conditional independencetest for Weights of Evidence modeling [J]. Natural Resources Research, 2002, 11(4): 249–255.
[127] Agterberg F P, Bonham-Carter G F, Wright D F.Statistical pattern integration for mineral exploration. In Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum, Gaál, G. and D. F. Merriam (Eds.), Pergamon Press, Oxford-New York, 1990, 1–21.
[128] An P, Moon, W M, Rencz A. Integration of geological, geophysical, and remote sensing data using fuzzy set theory [J]. Canadian Journal of Exploration Geophysics, 1991, 27 (1): 1–11.
[129] An P, Moon, W M, Bonham-Carter, G F. Uncertainty management in integration of exploration data using the belief function [J]. Nonrenewable Resources, 1994, 3(1): 60–71.
[130] Aspinall P J, Hill A R. Clinical inferences and decisions—I. Diagnosis and Bayes’theorem [J]. Ophthalmic and Physiologic Optics, 1983, 3: 295–304.
[131] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J]. Geomorphology, 2005, 65: 15–31.
[132] Bian Q T, Li D H, Pospelov I, et al. Age, geochemistry and tectonic setting of Buqingshan ophiolites, North Qinghai-Tibet Plateau, China [J]. Journal of Asian Earth Sciences, 2004, 23: 577–596.
[133] Bierlein F P, Murphy F C, Weinberg R F. Distribution of orogenic gold deposits in relation to fault zones and gravity gradients: targeting tools applied to the Eastern Goldfields, Yilgarn Craton, Western Australia [J]. Mineralium Deposita, 2006, 41(2): 107–126.
[134] Brown W, Groves D, Gedeon T. Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping [J]. Natural Resources Research, 2003, 12(3): 183–200.
[135] Bárdossy G, Fodor J. Evaluation of Uncertainties and Risks in Geology: New Mathematical Approa- ches to their Handling [M]. Springer-Verlag, Berlin Heidelberg, 2004, 1–221.
[136] Bonham-Carter G.F. Geographic systems for geoscientists: modeling with GIS [M]. Pergamon Press, Oxford, 1994, 1-398.
[137] Bonham-Carter G F, Agterberg F P, Wright D F. Weights of evidence modelling: a new approach to mapping mineral potential [C]. In Agterberg, F.P., and G.F. Bonham-Carter, (Eds.), Statistical Applications in the Earth Sciences: Geol. Survey Canada Paper, 1989, 89–90:171–183.
[138] Bonham-Carter G F, Agterberg F P, Wright D F. Integration of geological data sets, for gold exploration in Nova Scotia [J]. Photogrammetry and Remote Sensing, 1988, 54: 1585–1592.
[139] Carranza E J M, Hale M. Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines [J]. Exploration and Mining Geology, 2001, 10(3): 165–175.
[140] Carranza E J M, Hale M. Spatial Association of Mineral Occurrences and Curvilinear Geological Features [J]. Mathematical Geology, 2002, 34(2): 203–221.
[141] Carranza E J M, Hale M. Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines [J]. Natural Resources Research, 2000, 9(3): 237–253.
[142] Carranzaa E J M, Hale M. Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines [J]. Ore Geology Reviews, 2003, 22(1-2): 117–132.
[143] Carranza E J M. Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines [J]. Natural Resources Research, 2004, 13(3): 173–187.
[144] Chang-Jo F C. Integrated computer system for use in evaluation of mineral and energy resources [J]. Mathmatical Geology, 1983, 10(5): 441–472.
[145] Choi S, Moon W M, Choi S G. Fuzzy logic fusion of W-Mo exploration data from Seobyeog-ri, Korea [J]. Geosciences Journal, 2000, 4(2): 43–52.
[146] Chernicoff C J, Richards J P, Zappettini E O. Crustal lineament control on magmatism and mineralization in northwestern Argentina: Geological, geophysical, and remote sensing evidence [J]. Ore Geology Reviews, 2002, 21: 127–155.
[147] Cheng Q, Agterberg F P. Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping [J]. Natural Resources Research, 1999, 8(1): 27–35.
[148] Cheng Q. Weights of evidence modeling of flowing wells in the Greater Toronto Area, Canada [J]. Natural Resources Research, 2004, 13(2): 77–86.
[149] Cheng Q. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China [J]. Ore Geology Reviews, 2007, 32: 314–324.
[150] Chung C F, Fabbri A G. The representation of geosciences information for data integration [J]. Nonrenewable Resources, 1993, 2(2): 123–139.
[151] Clowes R M, Hammer P T C, Viejo G F, et al. Lithospheric structure in northwestern Canada from LITHOPROBE seismic refraction and related studies: A synthesis [J]. Canadian Journal of Earth Sciences, 2005, 42(6): 1277–1293.
[152] Corsini A, Cervi F, Ronchetti F. Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy) [J]. Geomorphology, 2009, 111: 79–87.
[153] Daneshfar B, Desrochers A, Budkewitsch P. Mineral-potential mapping for MVT deposits with limited data sets using Landsat data and geological evidence in the Borden basin, northern Baffin island, Nunavut, Canada [J]. Natural Resources Research, 2006, 15(3): 129–149.
[154] Davis J C. Statistics and Data Analysis in Geology [M], 2nd Edition, John Wiley and Sons, Singapore, 1973, 1–550.
[155] Eeckhaut M V D, Vanwalleghem T, Poesen J, et al. Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium) [J]. Geomorphology, 2006, 76: 392–410.
[156] Franklin J M, Gibson H L, Jonasson I R, et al. Volcanogentic massive sulfide deposits [C]. Economic Geology 100th Anniversary Volume, 2005, 523–560.
[157] Golebya B R, Blewetta R S, Korsch R J, et al. Deep seismic reflection profiling in the Archaean northeastern Yilgarn Craton, Western Australia: implications for crustal architecture and mineral potential [J]. Tectonophysics, 2004, 388: 119–133.
[158] Griffiths J C. Mineral resource assessment using the unit regional value concept [J]. Mathmatical Geology, 1978, 10(5): 441-472.
[159] Harris D P. A Subjective Probability Appraisal of Metal Endowment of Northern Sonora, Mexico [J]. Economic Geology, 1973, (65): 222–242.
[160] Hamby D M. A Review of Techniques for Parameter Sensitivity Analysis of Environmental Models [J]. Environmental Monitoring and Assessment, 1994, 32 (2): 135–154.
[161] Helton J C, Garner J W, McCurley R D, et al. Sensitivity Analysis Techniques and Results forPerformance Assessment at the Waste Isolation Pilot Plant [R]. Albuquerque, NM: Sandia National Laboratory, 1991, Report No. SAND 90-7103.
[162] Helton J C, Iman R L, Brown J B. Sensitivity Analysis of the Asymptotic Behavior of a Model for the Environmental Movement of Radionuclides [J]. Ecological Modelling, 1985, 28: 243–278.
[163] Hronsky J M A, Groves D I. Science of targeting: Definition, strategies, targeting and performance measurement [J]. Australian Journal of Earth Sciences, 2008, 55: 3–12.
[164] Kemp L D, Bonham-Carter G F, Raines G L. Arc-WofE: ArcView extension for weights of evidence mapping. 1999, http: //gis.nrcan.gc.ca/software/arcview/ wofe.
[165] Kim T W, Chang S H, Lee B H. Uncertainty and Sensitivity Analyses in Evaluating Risk of High-Level Waste Repository [J]. Radiation, Waste Management and the Nuclear Fuel Cycle, 1988, 10: 321–356.
[166] Koike K, Matsuda S, Suzuki T, et al. Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District, Northern Japan,for Exploring Kuroko-Type Deposits [J]. Natural Resources Research, 2002, 11(2): 135–156.
[167] Labovitz M L, Griffiths J C. An Inventory of Undiscovered Canadian Mineral Resources [J]. Economic Geology, 1982, 77: 1642–1654.
[168] Large R R, McPhie J, Gemmell J B, et al. The spectrum of ore deposit types, volcanic environments, alteration halos, and related exploration vectors in submarine volcanic successions some examples from Australia [J]. Economic Geology, 2001, 96: 913–938.
[169] Liu Y, Genser T J, Neubauer F, et al. 40Ar/39Ar mineral ages from basement rocks in the Eastern Kunlun Mountains, NW China, and their tectonic implications [J]. Tectonophysics, 2005, 398: 199–224.
[170] Luo X, Dimitrakopoulos R. Data-driven fuzzy analysis in quantitative mineral resource assessment [J]. Computers & Geosciences, 2003, 29(1): 3–13.
[171] Malehmir A T, Tryggvason A. The Paleoproterozoic Kristineberg mining area, northern Sweden: re- sults from integrated 3D geophysical and geologic modelling, and implicat ions for targeting ore deposits [J]. Geophysics, 2009, 74(1): 9–22.
[172] Masetti M, Poli S, Sterlacchini S. The use of the weights-of-evidence model technique to estimate the vulnerability of groundwater to nitrate contamination [J]. Natural Resources Research, 2007, 16 (2): 109–119.
[173] McCarthy M A, Burgman M A, Ferson S. Sensitivity Analysis for Models of Population Viability [J]. Biological Conservation, 1995, 73(1): 93–100.
[174] Milkereit B, Berrer E K, King A R, et al. Development of 3D seismic exploration technology for deep nickel copper deposits-a case history from the Sudbury basin, Canada [J]. Geophysics, 2000, 65(6): 1890–1899.
[175] Neuh?user B, Terhorst B. Landslide susceptibility assessment using″weights-of- evidence″applied to a study area at the Jurassic escarpment (SW-Germany) [J]. Geomorphology, 2007, 86: 12–24.
[176] Nyk?nen V, Ojala V J. Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the northern Fennoscandian Shield, Finland [J]. Natural Resources Research, 2007, 16(2): 85–92.
[177] Nyk nen V. Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield [J]. Natural Resources Research, 2008, 17(1): 29–48.
[178] Ohlmacher G C, Davis J C. Using multiple logistic regression and GIS technology to predictlandslide hazard in northeast Kansas, USA [J]. Engineering Geology, 2003, 69(3-4): 331–343.
[179] Pan G C. Extended weights of evidence modeling for the pseudo-estimation of metal grades [J]. Nonrenewable Resources, 1996, 5(1): 53-76.
[180] Pan Y S, Zhou W M, Xu R H, et al. Geological characteristics and evolution of the Kunlun Mountains region during the early Paleozoic [J]. SCIENCE IN CHINA (Series D), 1996, 39(4): 337–347.
[181] Porwal A, Carranza E J M, Hale M. Extended Weights-of-Evidence Modeling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India [J]. Explor Mining Geol, 2001, 10 (4): 273–287.
[182] Porwal A, Carranza E J M, Hale M. A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping [J]. Mathematical Geology, 2004, 36(7): 803–826.
[183] Porwal A, Carranza E J M., Hale M. A Hybrid fuzzy weights-of-evidence model for mineral potential mapping [J]. Natural Resources Research, 2006, 15(1): 1–14.
[184] Porwal A, Kreuzer O. Introduction to the Special Issue: Mineral prospectivity analysis and quanti- tative resource estimation [J]. Ore Geology Reviews, 2010, 38: 121–127.
[185] Ranjbar H, Honarmand M, Moezifar Z. Application of the Crosta technique for porphyry copper alteration mapping, using ETM+ data in the southern part of the Iranian volcanic sedimentary belt [J]. Journal of Asian Earth Sciences, 2004, 24: 237–243.
[186] Rigol-Sanchez J P, Chica-Olmo M, Abarca-Hernandez F. Artificial neural networks as a tool for mineral potential mapping with GIS [J]. International Journal of Remote Sensing, 2003, 24(5): 1151–1156.
[187] Romero C R, Luque S. Habitat quality assessment using Weights-of-Evidence based GIS modeling: The case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest [J]. Ecological Modeling, 2006, 196: 62–76.
[188] Rowan L C, Schmidt R G, Mars C J. Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data [J]. Remote Sensing of Environment, 2006, 104: 74–87.
[189] Sahoo N R, Pandala H S. Integration of Sparse Geologic Information in Gold Targeting Using Logistic Regression Analysis in the Hutti-Maski Schist Belt, Raichur, Karnataka, India—A Case Study [J]. Natural Resources Research, 1999, 8(3): 233–250.
[190] Samal A R, Mohanty M K, Fifarek R H. Backward elimination procedure for a predictive model of gold concentration [J]. Journal of Geochemical Exploration, 2008, 97: 69–82.
[191] Sangster D F. Mississippi Valley-type and Sedex lead-zinc deposits-a comparative examination: Institution of Mining and Metallurgy Transactions [J]. Section B, Applied Earth Sciences, 1990, 99: 21–42.
[192] Sillitoe R H. Gold-rich porphyry deposits: descriptive and genetic models and their role in exploration and discovery [J]. Reviews in Economic Geology, 2000, 13: 315–345.
[193] Singer D A. Basic concepts in three-part quantitative assessments of undiscovered mineral resources [J]. Nonrenewable Resources, 1993, 2(2): 69–81.
[194] Spiegelhalter D J, Knill-Jones R P. Statistical and knowledge-based approaches clinical decision support systems, with an application in gastroenterology [J]. Journal of the Royal Statistical Society (A), 1984, 1: 35–77.
[195] Sun Y, Seccombe P K, Yang K. Application of short-wave infrared spectroscopy to define alteration zones associated with the Elura zinc–lead–silver deposit, NSW, Australia [J]. Journal of GeochemicalExploration, 2001, 73(1): 11–26.
[196] Scott M, Dimitrakopoulos R. Quantitative Analysis of Mineral Resources for Strategic Planning: Im- plications for Australian Geological Surveys [J]. Natural Resources Research, 2001, 10(3): 159–177.
[197] Song R, Hiromu D, Kazutoki A, et al. Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan [J]. International Journal of Sediment Research, 2008, 23: 106–118.
[198] Urabe T, Bake E T. Ishibashi J, et al. The effect of magmatic activity on hydrothermal venting along the superfast-spreading East Pacific Rise [J]. Science, 1995, 269: 1092–1095.
[199] Wright D F, Bonham-Carter G F. VHMS favourability mapping with GIS-based integration models, Chisel Lake–Anderson Lake area [C]. In: EXTECH 1: A Multidisciplinary Approach to Massive Sulphide Research in Rusty Lake–Snow Lake Greenstone Belts, Manitoba. Bonham-Carter, G.F., Gally, A.G., Hall, G.E.M. (Eds.), Geological Survey of Canada, Bulletin, 1996, 426: 339–376.
[200] Yang J S, Robinson P T, Jiang C F, et al. Ophiolites of the Kunlun Mountains, China and their tectonic implications [J]. Tectonophysics, 1996, 258: 215–231.
[201] Yilmaz I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey) [J]. Computers & Geosciences, 2009, 35: 1125–1138.
[202] Zhang X, Pazner M, Duke N. Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California) [J]. ISPRS Journal of Photo- grammetry & Remote Sensing, 2007, 62(2): 271–282.
[2]边千韬,赵大升,叶正仁,等.初论昆祁秦缝合系[J].地球学报, 2002, 23(6): 501–508.
[3]程裕琪,陈毓川,赵一鸣.初论矿床的成矿系列问题[J].中国地质科学院院报, 1979, 1(1): 32–58.
[4]程裕淇.再论矿床的成矿系列问题[J].中国地质科学院院报, 1983, 1–64.
[5]陈毓川.当代矿产资源勘查评价的理论与方法[M] .北京:地震出版社. 1999. 1-549.
[6]陈毓川,裴荣富,宋天锐,等.中国矿床成矿系列初论[M].北京:地质出版社, 1998, 1–121.
[7]陈毓川等.中国成矿体系与区域成矿评价[M].北京:地质出版社, 2007, 1-1004.
[8]陈能松,孙敏,王勤燕,等.东昆仑造山带中带的锆石U-Pb定年与构造演化启示[J].中国科学D辑:地球科学, 2008, 38(6): 657–666.
[9]陈能松,何蕾,孙敏,等.东昆仑造山带早古生代变质峰期和逆冲构造变形年代的精确限定[J].科学通报, 2002, 47(8): 628–631.
[10]陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社, 1999, 1–240.
[11]谌宏伟,罗照华,莫宣学,等.东昆仑造山带三叠纪岩浆混合成因花岗岩的岩浆底侵作用机制[J].中国地质, 2005, 32(3): 385–395.
[12]陈守建,李荣社,计文化,等.昆仑造山带二叠纪岩相古地理特征及盆山转换探讨[J].中国地质,2010, 37(2): 374–393.
[13]陈守建,李荣社,计文化,等.昆仑造山带石炭纪岩相特征及构造古地理[J].地球科学与环境学报, 2008, 30(3): 221–233.
[14]成秋明.非线性成矿预测理论:多重分形奇异性–广义自相似性-分形谱系模型与方法[J].地球科学–中国地质大学学报, 2006, 31(3): 337–348.
[15]成秋明.地质异常的奇异性度量与隐伏源致矿异常识别[J].地球科学–中国地质大学学报, 2011, 36(2): 307-316.
[16]成秋明.成矿过程奇异性与矿产预测定量化的新理论与新方法[J].地学前缘, 2007, 14(5): 42–53.
[17]段吉业,葛肖虹.中国西北地区各构造单元之间地层和生物古地理的亲缘关系-兼论西北地区构造格局[J].地质通报, 2005, 24(6): 558–563.
[18]党兴彦,范桂忠,李智明,等.东昆仑成矿带典型矿床分析[J].西北地质, 2006, 39(2): 143–155.
[19]党兴彦,李智明.青海昆仑山口成矿区矿产资源潜力评价[R].青海省地质调查院, 2004.
[20]丁正江,孙丰月,李碧乐.青海省苦海汞(金)矿床地质特征及找矿前景分析[J].地质与勘探, 2010, 46(2): 198–206.
[21]董树文,项怀顺,高锐,等.长江中下游庐江–枞阳火山岩矿集区深部结构与成矿作用[J].岩石学报, 2010, 26(6): 2529–2542.
[22]丰成友,张德全,党兴彦,等.青海格尔木地区驼路沟钴(金)矿床石英钠长石岩锆石SHRIMPU -Pb定年对―纳赤台群‖时代的制约[J].地质通报, 2005, 24(6): 501–505.
[23]丰成友,张德全,屈文俊,等.青海格尔木驼路沟喷流沉积型钴(金)矿床的黄铁矿Re–Os定年[J].地质学报, 2006, 80(4): 571–576.
[24]丰成友,张德全,王富春,等.青海东昆仑复合造山过程及典型造山型金矿地质[J].地球学报, 2004, 25(4): 415–422.
[25]丰成友,李东生,吴正寿,等.青海东昆仑成矿带斑岩型矿床的确认及找矿前景分析[J].矿物学报,增刊, 2009, p. 171–172.
[26]葛肖虹,刘俊来.被肢解的―西域克拉通‖[J].岩石学报, 2000, 16(1): 59–66.
[27]何书跃,祁兰英,舒树兰,等.青海祁漫塔格地区斑岩铜矿的成矿条件和远景[J].地质与勘探, 2008, 44(2): 14–22.
[28]郭宪璞,王乃文,丁孝忠.东昆仑格尔木南部纳赤台群和万宝沟群基质系统与外来系统地球化学差异[J].地质通报, 2004, 23(12): 1188–1195.
[29]郭宪璞,王乃文,丁孝忠,等.青海东昆仑纳赤台群基质系统与外来系统的关系[J].地质通报, 2003, 22 (3): 160–164.
[30]郭正府,邓晋福,许志琴,等.青藏东昆仑晚古生代末–中生代中酸性火成岩与陆内造山过程[J].现代地质, 1998, 12(3): 344–352.
[31]郭晓东,张玉杰,刘桂阁,等.东昆仑地区金铜等成矿规律及找矿方向[J].黄金地质, 2004, 10(4): 16–22.
[32]高章鉴,罗才让,井继锋.青海省肯德可克金矿热水沉积层矽卡岩特征及成矿意义[J].西北地质, 2001, 34(2): 50–53.
[33]高晓峰,校培喜,谢从瑞,等.东昆仑阿牙克库木湖北巴什尔希花岗岩锆石LA-ICP-MS U-Pb定年及其地质意义[J].地质学报, 2010, 29(7): 1001–1008.
[34]高锐,卢占武,刘金凯,等.庐–枞金属矿集区深地震反射剖面解释结果–揭露地壳精细结构,追踪成矿深部过程[J].岩石学报, 2010, 26(9): 2543–2552.
[35]郝杰,刘小汉,桑海清.新疆东昆仑阿牙克岩体地球化学与40Ar–39Ar年代学研究及其大地构造意义[J].岩石学报, 2003, 19(3): 517–522.
[36]侯增谦.大陆碰撞成矿论[J].地质学报, 2010, 84(1): 30–58.
[37]姜春发,杨经绥,冯秉贵,等.昆仑开合构造[M].北京:地质出版社, 1992, 1–224.
[38]姜耀辉,芮行健,贺菊瑞,等.西昆仑加里东期花岗岩类构造的类型及大地构造意义[J].岩石学报, 1999, 15(1): 105–115.
[39]寇玉才,李战业,王英孝.尕林格矽卡岩型铁多金属矿床地质–地球物理模型[J].西北地质, 2010, 43(2): 20–31.
[40]李宏录,卫岗,曾宪刚,等.应用航磁资料在野马泉地区寻找以铁为主多金属矿产[J].物探与化探, 2009, 33(2): 117-122.
[41]李厚民,沈远超,胡正国,等.青海五龙沟金矿床矿石、矿物含金性及金的赋存状态[J].矿物学报, 2001, 21(1): 89–94.
[42]李世金,孙丰月,丰成友,等.青海东昆仑鸭子沟多金属矿的成矿年代学研究[J].地质学报, 2008a, 82(7): 949–955.
[43]李世金,孙丰月,王力,等.青海东昆仑卡尔却卡多金属矿区斑岩型铜矿的流体包裹体研究[J].矿床地质, 2008b, 27(3): 399–406.
[44]李东生,奎明娟,古凤宝,等.青海赛什塘铜矿床的地质特征及成因探讨[J].地质学报, 2009, 83(5): 719–730.
[45]李荣社,计文化,赵振明,等.昆仑早古生代造山带研究进展[J].地质通报, 2007,26 (4): 373–381.
[46]李光岑,林宝玉.昆仑山东段几个地质问题的探讨[C].青藏高原地质论文集. 1982, 28–48.
[47]李廷栋,肖序常.青藏高原地体构造分析-青藏高原岩石圈结构构造和形成演化[C].中华人民共和国地质矿产部地质专报, 1996, 20: 6–20.
[48]李智明,薛春纪,王晓虎,等.东昆仑区域成矿特征及有关找矿突破问题分析[J].地学评论, 2007, 53(5): 708–718.
[49]李裕伟.空间信息技术的发展及其在地球科学中的应用[J].地学前缘, 1998, 5(1-2): 335–341.
[50]李裕伟,赵精满,李晨阳. 2007.基于GMS/DSS和GIS的潜在矿产资源评价方法[M].地震出版社, 1-946.
[51]刘继庆,胡正国,钱壮志,等.东昆仑NW向线性构造带地质特征及找矿意义[J].西安工程学院学报, 2000, 22(2): 18–21.
[52]刘凤山,石准立.太行山燕山造山带与中生代花岗岩有关的金属矿床成矿系列特征[J].矿床地质, 1998, 17(3): 193–203.
[53]刘云华,莫宣学,张雪亭,等.东昆仑野马泉地区矽卡岩矿床地质特征及控矿条件[J].华南地质与矿产, 2005, 3: 18–23.
[54]刘云华,莫宣学,喻学惠,等.东昆仑野马泉地区景忍花岗岩锆石SHRIMP U-Pb定年及其地质意义[J].岩石学报, 2006, 22(10): 2457–2463.
[55]刘云华.东昆仑祁漫塔格地区多金属成矿作用及其与花岗岩的关系[D].北京:中国地质大学(北京),博士论文,2004,1–146.
[56]刘增铁,任家琪,邬介人,等.青海铜矿[M].北京:地质出版社, 2008, 1-291.
[57]刘成东,莫言学,罗照华,等.东昆仑壳–幔岩浆混合作用:来自锆石SHRIMP年代学的证据[J].科学通报, 2004, 49(6): 592–602.
[58]刘战庆,裴先治,李瑞保,等.东昆仑南缘阿尼玛卿构造带布青山地区两期蛇绿岩的LA-ICP-MS锆石U-Pb定年及其构造意义[J].地质学报, 2011, 85(2): 185-194.
[59]罗照华,柯珊,曹永清,等.东昆仑印支晚期幔源岩浆活动[J].地质通报, 2002, 21 (6): 292–297.
[60]罗照华,邓晋福,曹永清,等.青海省东昆仑地区晚古生代–早中生代火山活动与区域构造演化[J].现代地质, 1999, 13(1): 51–56.
[61]吕庆田,廉玉广,赵金花.反射地震技术在成矿地质背景与深部矿产勘查中的应用:现状与前景[J].地质学报, 2010a, 84(6): 771–787.
[62]吕庆田,韩立国,严加冰,等.庐枞矿集区火山气液型铁、硫矿床及控矿构造的反射地震成像[J].岩石学报, 2010b, 26(6): 2598–2612.
[63]吕庆田,侯增谦,史大年,等.铜陵狮子山金属矿地震反射结果及对区域成矿的意义[J].地质学报, 2004, 23(3): 390–398.
[64]梅燕雄,裴荣富,李进文,等.中国中生代矿床成矿系列类型及其演化[J].矿床地质, 2004, 23(2): 190–197.
[65]莫宣学,邓晋福.东昆仑中段铜金成矿条件及找矿方向的框架研究[R].北京:中国地质大学, 1998.
[66]潘桂堂,陈智梁,李兴振,等.东特提斯地质构造形成演化[M].北京:地质出版社, 1997.
[67]潘彤,马梅生,康祥瑞.东昆仑肯德可克及外围钴多金属矿找矿突破的启示[J].中国地质, 2001, 28(2): 17-20.
[68]潘彤,罗才让,伊有昌,等.青海省金属矿产成矿规律及成矿预测[M].北京:地质出版社, 2006, 1-227.
[69]潘裕生,周伟明,许荣华,等.昆仑山早古生代地质特征与演化[J].中国科学(D辑), 1996, 26(4): 302–607.
[70]钱壮志,胡正国,刘继庆.东昆仑北西向韧性剪切带发育的区域构造背景–以石灰沟韧性剪切带为例[J].成都理工学院学报, 1998, 25(2): 201–205.
[71]钱壮志,胡正国,刘继庆,等.古特提斯东昆仑活动陆缘及其区域成矿[J].大地构造与成矿学, 2000, 24(2): 134–139.
[72]青海省地质矿产局.区域地质调查报告(1:20万),格尔木市幅(J2462[35])、纳赤台幅(I2462[5])[R], 1981, 1–211.
[73]任涛,王瑞廷,王向阳,等.秦岭造山带柞水_山阳沉积盆地铜矿勘查思路与方法[J].地质学报, 2009, 83(11): 1730–1738.
[74]史仁灯,杨经绥,吴才来,等.青藏高原北部柴北缘超高压变质带中的岛弧火山岩:洋–陆俯冲伴随陆–陆俯冲的证据[J].地质学报, 2004, 78: 52–64.
[75]宋治杰,张汉文,李文明,等.青海鄂拉山地区铜多金属矿床的成矿条件及成矿模式[J].西北地质科学, 1999, 16(1): 134–144.
[76]佘宏全,张德全,景向阳,等.青海省乌兰乌珠尔斑岩铜矿床地质特征与成因[J].中国地质, 2007, 34(2): 306-314.
[77]肖克炎,丁建华,娄德波.试论成矿系列与矿产资源评价[J].矿床地质,2009, 28(3): 357–365.
[78]魏启荣,李德威,王国灿.东昆仑万保沟群火山岩(Pt2w)岩石地球化学特征及其构造背景[J].矿物岩石, 2007, 27(1): 97–106.
[79]吴庭祥,李宏录.青海尕林格地区铁多金属矿床的地质特征与地球化学特征[J].矿物岩石地球化学通报, 2009, 28(2): 157–161.
[80]吴健辉,丰成友,张德全,等.柴达木盆地南缘祁漫塔格-鄂拉山地区斑岩–矽卡岩矿床地质[J].矿床地质, 2010, 29(5): 760–774.
[81]王移生.青海日龙沟锡–多金属矿床地质特征及成矿作用[J].西北地质, 1990, 2: 43-48.
[82]王松,丰成友,李世金,等.青海祁漫塔格卡尔却卡铜多金属矿区花岗闪长岩锆石SHRIMPU-Pb测年及其地质意[J].中国地质, 2009, 36(1): 74-84.
[83]王登红,应立娟,王成辉,等.中国贵金属矿床的基本成矿规律与找矿方向[J].地学前缘, 2007, 14(5): 71–81.
[84]王国灿,王青海,简平,等.东昆仑前寒武纪基底变质岩系的锆石SHRIMP年龄及其构造意义[J].地学前缘, 2004, 11(4): 481–490.
[85]王国灿,张天平,梁斌,等.东昆仑造山带东段昆中复合蛇绿混杂岩带及―东昆中断裂带‖地质涵义[J].地球科学–中国地质大学学报, 1999, 24(2): 129–133.
[86]王国灿,向树元,王岸,等.东昆仑及相邻地区中生代-新生代早期构造过程的热年代学记录[J].地球科学-中国地质大学学报, 2007a, 32(5): 605–614.
[87]王国灿,魏启荣,贾春兴,等.关于东昆仑地区前寒武纪地质的几点认识[J].地质通报, 2007b, 26(8): 929–937.
[88]王秉璋,张智勇,张森琦,等.东昆仑东端苦海–赛什塘地区晚古生代蛇绿岩的地质特征[J].地球科学–中国地质大学学报, 2000, 25(6): 592-598.
[89]王惠初,陆松年,袁桂邦,等.柴达木盆地北缘滩间山群的构造属性及形成时代[J].地质通报, 2003, 22(7): 487-493.
[90]王世称,范继璋,杨永华.矿产资源评价[M].吉林:吉林科学技术出版社1990, 1–246
[91]王世称,陈永良,夏立显.综合信息矿产预测理论与方法[M].北京:科学出版社, 2002, 1–343.
[92]夏庆霖,成秋明,左仁广等.基于GIS矿产勘查靶区优选技术[J].中国地质大学–地球科学学报, 2009, 34(2): 287– 293.
[93]许志琴,崔军文,张建新.大陆山链变形构造动力学[M].北京:冶金工业出版社, 1996, 1–246.
[94]许志琴,杨经绥,嵇少丞,等.中国大陆构造及动力学若干问题的认识[J].地质学报, 2010, 84(1): 1–29.
[95]徐文艺,张德全,阎升好,等.东昆仑地区矿产资源大调查进展与前景展望[J].中国地质, 2001, 28(1): 25–29.
[96]尹安.喜马拉雅–青藏高原造山带地质演化–显生宙亚洲大陆生长[J].地球学报, 2001, 22(3): 193– 230.
[97]殷鸿福,张克信.东昆仑造山带的一些特点[J].地球科学–中国地质大学学报, 1997, 22(4): 339– 342.
[98]殷鸿福,张克信.中华人民共和国区域地质调查报告:冬给措纳湖幅( I47C001002),比例尺1∶250,000[R].武汉:中国地质大学出版社, 2003.
[99]杨经绥,许志琴,马昌前,等.复合造山作用和中国中央造山带的科学问题[J].中国地质, 2010, 37(1): 1–11.
[100]杨小斌,杨宝荣,王晓云.青海果洛龙洼金矿床金的赋存状态研究[J].地质与勘探, 2006, 42(5): 57–59.
[101]燕宁,李社宏,陆智平.青海省兴海县索拉沟铜多金属矿成矿地质特征与矿床成因[J].大地构造与成矿学, 2011, 35(1): 161-166.
[102]闫臻,胡正国,刘继庆,等.东昆仑开荒北金矿床地质特征及控矿条件[J].西安工程学院学报, 2000, 22(1): 23–27.
[103]叶天竺,肖克炎,严光.矿床模型综合地质信息预测技术研究[J].地学前缘, 2007, 14(5): 11-19.
[104]袁万明,莫宣学,喻学惠,等,东昆仑热液金成矿带及其找矿方向[J].地质与勘探, 2000, 36(5): 20–23.
[105]袁桂邦,王惠初,李惠民,等.柴北缘绿梁山地区辉长岩的锆石U-Pb年龄及意义[J].前寒武纪研究进展, 2002, 25(1): 36–39.
[106]张爱奎,莫宣学,李云平,等.青海西部祁漫塔格成矿带找矿新进展及其意义[J].地质通报, 2010, 29(7): 1062–1074.
[107]张德全,党兴彦,佘宏全,等.柴北缘–东昆仑地区造山型金矿床的Ar–Ar测年及其地质意义[J].矿床地质, 2005, 24(2): 87–98.
[108]张德全,王富春,佘宏全,等.柴北缘–东昆仑地区造山型金矿床的三级控矿构造系统[J].中国地质, 2007, 34(1): 92–100.
[109]张德全,佘宏全,徐文艺,等.驼路沟喷气沉积型钴(金)矿床成矿地质背景及矿床成因的地球化学限制[J].地球学报, 2002a, 23(6): 527–534.
[110]张德全,徐文艺,贾群子,等.东昆仑地区综合找矿预测与突破[R].北京:中国地质科学院, 2002b.
[111]张德全,丰成友,李大新,等.柴北缘-东昆仑地区的造山型金矿床[J].矿床地质, 2001, 20(2): 137–146.
[112]张汉文.青海铜峪沟铜矿床的矿化特征、环境和矿床类型[J].西北地质, 2001, 34 (4): 30–42.
[113]张建新,孟繁聪,万渝生,等.柴达木盆地南缘金水口群的早古生代构造热事件:锆石U-Pb SHRIMP年龄证据[J].地质通报, 2003, 22(6): 397–404.
[114]张智勇,殷鸿福,王秉璋,等.昆秦接合部海西期苦海–赛什塘分支洋的存在及其证据[J].地球科学–中国地质大学学报, 2004, 29(6): 691–696.
[115]朱云海,陈能松,王国灿,等.东昆中蛇绿岩中单斜辉石、角闪石矿物成分特征及岩石学意义[J].地球科学–中国地质大学学报, 1997, 22(4): 364-368.
[116]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].武汉:中国地质大学出版社, 1983,1–272.
[117]赵鹏大,池顺都.初论地质异常[J].地球科学–中国地质大学学报, 1991, 16(3): 241–248.
[118]赵鹏大.―三联式‖资源定量预测与评价-数字找矿理论与实践探讨[J].地球科学–中国地质大学学报[J], 2002, 27(5): 482–489.
[119]赵鹏大,陈建平,张寿庭.―三联式‖成矿预测新进展[J].地学前缘, 2003, 10(2): 455-463.
[120]赵鹏大,陈永清.科学选靶的理论与途径[J].地球科学–中国地质大学学报, 2011, 36(2): 181– 188.
[121]赵鹏大.成矿定量预测与深部找矿[J].地学前缘, 2007, 14(5): 1–10.
[122]邹长毅,史长义.五龙沟金矿区区域地球化学异常特征及找矿标志[J].中国地质, 2004, 31(4): 420–423.
[123] Agterberg F P. Application of image analysis and multivariate analysis to mineral resource appraisal [J]. Economic Geology, 1981, 76: 1016–1031.
[124] Agterberg F P, Divi S R. A statistical model for the distribution of copper, lead, and zinc in the Canadian Appalachian region [J]. Economic Geology, 1978, 73(2): 230–245.
[125] Agterberg F P, Bonham-Carter G F, Cheng Q, et al. Weights of evidence modeling and weighted logistic regression in mineral potential mapping [C]. In Davis, J. C., and Herzfeld, U. C., eds., Computers in Geology: Oxford Univ. Press, New York, 1993, p. 13–32.
[126] Agterberg F P, Cheng Q. Conditional independencetest for Weights of Evidence modeling [J]. Natural Resources Research, 2002, 11(4): 249–255.
[127] Agterberg F P, Bonham-Carter G F, Wright D F.Statistical pattern integration for mineral exploration. In Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum, Gaál, G. and D. F. Merriam (Eds.), Pergamon Press, Oxford-New York, 1990, 1–21.
[128] An P, Moon, W M, Rencz A. Integration of geological, geophysical, and remote sensing data using fuzzy set theory [J]. Canadian Journal of Exploration Geophysics, 1991, 27 (1): 1–11.
[129] An P, Moon, W M, Bonham-Carter, G F. Uncertainty management in integration of exploration data using the belief function [J]. Nonrenewable Resources, 1994, 3(1): 60–71.
[130] Aspinall P J, Hill A R. Clinical inferences and decisions—I. Diagnosis and Bayes’theorem [J]. Ophthalmic and Physiologic Optics, 1983, 3: 295–304.
[131] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J]. Geomorphology, 2005, 65: 15–31.
[132] Bian Q T, Li D H, Pospelov I, et al. Age, geochemistry and tectonic setting of Buqingshan ophiolites, North Qinghai-Tibet Plateau, China [J]. Journal of Asian Earth Sciences, 2004, 23: 577–596.
[133] Bierlein F P, Murphy F C, Weinberg R F. Distribution of orogenic gold deposits in relation to fault zones and gravity gradients: targeting tools applied to the Eastern Goldfields, Yilgarn Craton, Western Australia [J]. Mineralium Deposita, 2006, 41(2): 107–126.
[134] Brown W, Groves D, Gedeon T. Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping [J]. Natural Resources Research, 2003, 12(3): 183–200.
[135] Bárdossy G, Fodor J. Evaluation of Uncertainties and Risks in Geology: New Mathematical Approa- ches to their Handling [M]. Springer-Verlag, Berlin Heidelberg, 2004, 1–221.
[136] Bonham-Carter G.F. Geographic systems for geoscientists: modeling with GIS [M]. Pergamon Press, Oxford, 1994, 1-398.
[137] Bonham-Carter G F, Agterberg F P, Wright D F. Weights of evidence modelling: a new approach to mapping mineral potential [C]. In Agterberg, F.P., and G.F. Bonham-Carter, (Eds.), Statistical Applications in the Earth Sciences: Geol. Survey Canada Paper, 1989, 89–90:171–183.
[138] Bonham-Carter G F, Agterberg F P, Wright D F. Integration of geological data sets, for gold exploration in Nova Scotia [J]. Photogrammetry and Remote Sensing, 1988, 54: 1585–1592.
[139] Carranza E J M, Hale M. Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines [J]. Exploration and Mining Geology, 2001, 10(3): 165–175.
[140] Carranza E J M, Hale M. Spatial Association of Mineral Occurrences and Curvilinear Geological Features [J]. Mathematical Geology, 2002, 34(2): 203–221.
[141] Carranza E J M, Hale M. Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines [J]. Natural Resources Research, 2000, 9(3): 237–253.
[142] Carranzaa E J M, Hale M. Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines [J]. Ore Geology Reviews, 2003, 22(1-2): 117–132.
[143] Carranza E J M. Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines [J]. Natural Resources Research, 2004, 13(3): 173–187.
[144] Chang-Jo F C. Integrated computer system for use in evaluation of mineral and energy resources [J]. Mathmatical Geology, 1983, 10(5): 441–472.
[145] Choi S, Moon W M, Choi S G. Fuzzy logic fusion of W-Mo exploration data from Seobyeog-ri, Korea [J]. Geosciences Journal, 2000, 4(2): 43–52.
[146] Chernicoff C J, Richards J P, Zappettini E O. Crustal lineament control on magmatism and mineralization in northwestern Argentina: Geological, geophysical, and remote sensing evidence [J]. Ore Geology Reviews, 2002, 21: 127–155.
[147] Cheng Q, Agterberg F P. Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping [J]. Natural Resources Research, 1999, 8(1): 27–35.
[148] Cheng Q. Weights of evidence modeling of flowing wells in the Greater Toronto Area, Canada [J]. Natural Resources Research, 2004, 13(2): 77–86.
[149] Cheng Q. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China [J]. Ore Geology Reviews, 2007, 32: 314–324.
[150] Chung C F, Fabbri A G. The representation of geosciences information for data integration [J]. Nonrenewable Resources, 1993, 2(2): 123–139.
[151] Clowes R M, Hammer P T C, Viejo G F, et al. Lithospheric structure in northwestern Canada from LITHOPROBE seismic refraction and related studies: A synthesis [J]. Canadian Journal of Earth Sciences, 2005, 42(6): 1277–1293.
[152] Corsini A, Cervi F, Ronchetti F. Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy) [J]. Geomorphology, 2009, 111: 79–87.
[153] Daneshfar B, Desrochers A, Budkewitsch P. Mineral-potential mapping for MVT deposits with limited data sets using Landsat data and geological evidence in the Borden basin, northern Baffin island, Nunavut, Canada [J]. Natural Resources Research, 2006, 15(3): 129–149.
[154] Davis J C. Statistics and Data Analysis in Geology [M], 2nd Edition, John Wiley and Sons, Singapore, 1973, 1–550.
[155] Eeckhaut M V D, Vanwalleghem T, Poesen J, et al. Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium) [J]. Geomorphology, 2006, 76: 392–410.
[156] Franklin J M, Gibson H L, Jonasson I R, et al. Volcanogentic massive sulfide deposits [C]. Economic Geology 100th Anniversary Volume, 2005, 523–560.
[157] Golebya B R, Blewetta R S, Korsch R J, et al. Deep seismic reflection profiling in the Archaean northeastern Yilgarn Craton, Western Australia: implications for crustal architecture and mineral potential [J]. Tectonophysics, 2004, 388: 119–133.
[158] Griffiths J C. Mineral resource assessment using the unit regional value concept [J]. Mathmatical Geology, 1978, 10(5): 441-472.
[159] Harris D P. A Subjective Probability Appraisal of Metal Endowment of Northern Sonora, Mexico [J]. Economic Geology, 1973, (65): 222–242.
[160] Hamby D M. A Review of Techniques for Parameter Sensitivity Analysis of Environmental Models [J]. Environmental Monitoring and Assessment, 1994, 32 (2): 135–154.
[161] Helton J C, Garner J W, McCurley R D, et al. Sensitivity Analysis Techniques and Results forPerformance Assessment at the Waste Isolation Pilot Plant [R]. Albuquerque, NM: Sandia National Laboratory, 1991, Report No. SAND 90-7103.
[162] Helton J C, Iman R L, Brown J B. Sensitivity Analysis of the Asymptotic Behavior of a Model for the Environmental Movement of Radionuclides [J]. Ecological Modelling, 1985, 28: 243–278.
[163] Hronsky J M A, Groves D I. Science of targeting: Definition, strategies, targeting and performance measurement [J]. Australian Journal of Earth Sciences, 2008, 55: 3–12.
[164] Kemp L D, Bonham-Carter G F, Raines G L. Arc-WofE: ArcView extension for weights of evidence mapping. 1999, http: //gis.nrcan.gc.ca/software/arcview/ wofe.
[165] Kim T W, Chang S H, Lee B H. Uncertainty and Sensitivity Analyses in Evaluating Risk of High-Level Waste Repository [J]. Radiation, Waste Management and the Nuclear Fuel Cycle, 1988, 10: 321–356.
[166] Koike K, Matsuda S, Suzuki T, et al. Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District, Northern Japan,for Exploring Kuroko-Type Deposits [J]. Natural Resources Research, 2002, 11(2): 135–156.
[167] Labovitz M L, Griffiths J C. An Inventory of Undiscovered Canadian Mineral Resources [J]. Economic Geology, 1982, 77: 1642–1654.
[168] Large R R, McPhie J, Gemmell J B, et al. The spectrum of ore deposit types, volcanic environments, alteration halos, and related exploration vectors in submarine volcanic successions some examples from Australia [J]. Economic Geology, 2001, 96: 913–938.
[169] Liu Y, Genser T J, Neubauer F, et al. 40Ar/39Ar mineral ages from basement rocks in the Eastern Kunlun Mountains, NW China, and their tectonic implications [J]. Tectonophysics, 2005, 398: 199–224.
[170] Luo X, Dimitrakopoulos R. Data-driven fuzzy analysis in quantitative mineral resource assessment [J]. Computers & Geosciences, 2003, 29(1): 3–13.
[171] Malehmir A T, Tryggvason A. The Paleoproterozoic Kristineberg mining area, northern Sweden: re- sults from integrated 3D geophysical and geologic modelling, and implicat ions for targeting ore deposits [J]. Geophysics, 2009, 74(1): 9–22.
[172] Masetti M, Poli S, Sterlacchini S. The use of the weights-of-evidence model technique to estimate the vulnerability of groundwater to nitrate contamination [J]. Natural Resources Research, 2007, 16 (2): 109–119.
[173] McCarthy M A, Burgman M A, Ferson S. Sensitivity Analysis for Models of Population Viability [J]. Biological Conservation, 1995, 73(1): 93–100.
[174] Milkereit B, Berrer E K, King A R, et al. Development of 3D seismic exploration technology for deep nickel copper deposits-a case history from the Sudbury basin, Canada [J]. Geophysics, 2000, 65(6): 1890–1899.
[175] Neuh?user B, Terhorst B. Landslide susceptibility assessment using″weights-of- evidence″applied to a study area at the Jurassic escarpment (SW-Germany) [J]. Geomorphology, 2007, 86: 12–24.
[176] Nyk?nen V, Ojala V J. Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the northern Fennoscandian Shield, Finland [J]. Natural Resources Research, 2007, 16(2): 85–92.
[177] Nyk nen V. Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield [J]. Natural Resources Research, 2008, 17(1): 29–48.
[178] Ohlmacher G C, Davis J C. Using multiple logistic regression and GIS technology to predictlandslide hazard in northeast Kansas, USA [J]. Engineering Geology, 2003, 69(3-4): 331–343.
[179] Pan G C. Extended weights of evidence modeling for the pseudo-estimation of metal grades [J]. Nonrenewable Resources, 1996, 5(1): 53-76.
[180] Pan Y S, Zhou W M, Xu R H, et al. Geological characteristics and evolution of the Kunlun Mountains region during the early Paleozoic [J]. SCIENCE IN CHINA (Series D), 1996, 39(4): 337–347.
[181] Porwal A, Carranza E J M, Hale M. Extended Weights-of-Evidence Modeling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India [J]. Explor Mining Geol, 2001, 10 (4): 273–287.
[182] Porwal A, Carranza E J M, Hale M. A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping [J]. Mathematical Geology, 2004, 36(7): 803–826.
[183] Porwal A, Carranza E J M., Hale M. A Hybrid fuzzy weights-of-evidence model for mineral potential mapping [J]. Natural Resources Research, 2006, 15(1): 1–14.
[184] Porwal A, Kreuzer O. Introduction to the Special Issue: Mineral prospectivity analysis and quanti- tative resource estimation [J]. Ore Geology Reviews, 2010, 38: 121–127.
[185] Ranjbar H, Honarmand M, Moezifar Z. Application of the Crosta technique for porphyry copper alteration mapping, using ETM+ data in the southern part of the Iranian volcanic sedimentary belt [J]. Journal of Asian Earth Sciences, 2004, 24: 237–243.
[186] Rigol-Sanchez J P, Chica-Olmo M, Abarca-Hernandez F. Artificial neural networks as a tool for mineral potential mapping with GIS [J]. International Journal of Remote Sensing, 2003, 24(5): 1151–1156.
[187] Romero C R, Luque S. Habitat quality assessment using Weights-of-Evidence based GIS modeling: The case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest [J]. Ecological Modeling, 2006, 196: 62–76.
[188] Rowan L C, Schmidt R G, Mars C J. Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data [J]. Remote Sensing of Environment, 2006, 104: 74–87.
[189] Sahoo N R, Pandala H S. Integration of Sparse Geologic Information in Gold Targeting Using Logistic Regression Analysis in the Hutti-Maski Schist Belt, Raichur, Karnataka, India—A Case Study [J]. Natural Resources Research, 1999, 8(3): 233–250.
[190] Samal A R, Mohanty M K, Fifarek R H. Backward elimination procedure for a predictive model of gold concentration [J]. Journal of Geochemical Exploration, 2008, 97: 69–82.
[191] Sangster D F. Mississippi Valley-type and Sedex lead-zinc deposits-a comparative examination: Institution of Mining and Metallurgy Transactions [J]. Section B, Applied Earth Sciences, 1990, 99: 21–42.
[192] Sillitoe R H. Gold-rich porphyry deposits: descriptive and genetic models and their role in exploration and discovery [J]. Reviews in Economic Geology, 2000, 13: 315–345.
[193] Singer D A. Basic concepts in three-part quantitative assessments of undiscovered mineral resources [J]. Nonrenewable Resources, 1993, 2(2): 69–81.
[194] Spiegelhalter D J, Knill-Jones R P. Statistical and knowledge-based approaches clinical decision support systems, with an application in gastroenterology [J]. Journal of the Royal Statistical Society (A), 1984, 1: 35–77.
[195] Sun Y, Seccombe P K, Yang K. Application of short-wave infrared spectroscopy to define alteration zones associated with the Elura zinc–lead–silver deposit, NSW, Australia [J]. Journal of GeochemicalExploration, 2001, 73(1): 11–26.
[196] Scott M, Dimitrakopoulos R. Quantitative Analysis of Mineral Resources for Strategic Planning: Im- plications for Australian Geological Surveys [J]. Natural Resources Research, 2001, 10(3): 159–177.
[197] Song R, Hiromu D, Kazutoki A, et al. Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan [J]. International Journal of Sediment Research, 2008, 23: 106–118.
[198] Urabe T, Bake E T. Ishibashi J, et al. The effect of magmatic activity on hydrothermal venting along the superfast-spreading East Pacific Rise [J]. Science, 1995, 269: 1092–1095.
[199] Wright D F, Bonham-Carter G F. VHMS favourability mapping with GIS-based integration models, Chisel Lake–Anderson Lake area [C]. In: EXTECH 1: A Multidisciplinary Approach to Massive Sulphide Research in Rusty Lake–Snow Lake Greenstone Belts, Manitoba. Bonham-Carter, G.F., Gally, A.G., Hall, G.E.M. (Eds.), Geological Survey of Canada, Bulletin, 1996, 426: 339–376.
[200] Yang J S, Robinson P T, Jiang C F, et al. Ophiolites of the Kunlun Mountains, China and their tectonic implications [J]. Tectonophysics, 1996, 258: 215–231.
[201] Yilmaz I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey) [J]. Computers & Geosciences, 2009, 35: 1125–1138.
[202] Zhang X, Pazner M, Duke N. Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California) [J]. ISPRS Journal of Photo- grammetry & Remote Sensing, 2007, 62(2): 271–282.