| 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. |
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