研究程度较低地区的矿产资源潜力评估方法应用研究

Application on potential evaluation of mineral resources in prospecting area with lower degree in research

具一定数量并有确定储量的模型单元是建立矿产资源潜力预测模型的基础。在勘探程度较低地区已知储量的模型单元极少条件下,为了开展资源潜力评估,引用丰度估计法、聚类分析和加权丰度进行银矿资源的潜力预测,并对其不同预测结果进行比较研究和综合评价,其预测成果具有一定可靠性。聚类分析谱系图直观,可综合利用地质、物探、化探、遥感多个变量对样本进行分类,其结果比一般传统分类方法更全面合理;丰度估计法适用于成矿规律相类似地区及以矿石矿物形式存在的元素含量的分析;加权丰度适合于多矿种的矿化系列的靶区定量预测。当研究区模型单元很少时,预测方法的采用,应取决于研究区资料收集的类型及数量等:如地质、物探、化探、遥感资料较齐全,就用聚类分析,结合另两种方法进行相互印证。对模型单元所提取的统计参数要尽可能代表研究区单元所具有的特征。

Acertain numbers of model units with a precise quantity of reserves, are the basis for the establishment of the models for prediction of mineral resource potentiality, In an area with lower degree in prospecting where model units are extremely few, the authors introduced the abundance estimate approach, cluster analysis and weighted abundance in the predicting of silver reserves potentiality for the evaluations. By comparing the different predicting results and a comprehensive evaluation, the reliability of the predicting results has been concluded. The pedigree chart of cluster analysis is more intuitionistic, and in this way we can comprehensively utilize the multivariables of geology, physical exploration, geochemical exploration and remote sensing to sort the samples, which is more comprehensive and logical in results than traditional sorting methods. The abundance estimate approach is more applicable in the area with similar mineralization law and in the analysis of element content in the form of ore minerals. Weighted abundance approach is suitable for the quantitative prediction in a target area with multi-mineral mineralizing series. When the model units in the research region are few, The predicting method in application depended on the amount of available data in the research region. If the information of geology, geonhysics, geochemistry catagories and remote sensing is complete for the exploration, the cluster analysis can be used, with mutual confirms by the other two methods at the same time. The statistical parameters in the model units should best represent the main characteristics of the research region units.