【摘要】 通过查明数字间的相关关系,研究、解决地质问题的大数据研究思想和方法,已被越来越多的地质工作者接受并应用。文中应用大数据思想、方法对甘肃省西秦岭地区1∶20万区域水系沉积物测量数据进行深度挖掘,构建起"基于化探信息的区域Au找矿靶区定量优选系列模型"。系列模型不但对研究区Au找矿靶区做出了精确的定量预测(经随机抽样查证,在1/3的预测靶区发现Au矿化),同时获得了与Au成矿地球化学理论结果高度一致的元素组合,并且对各元素在找矿靶区预测模型中的重要程度做出定量评价,为进一步研究矿床成因和控矿条件提供了定量依据。该研究结果充分说明海量数据信息中隐藏着极大的潜能,依据地质大数据的思想和定量研究方法就能将其充分地挖掘出来。同时这也充分证明了通过"查明数据间的相关关系取代分析事物因果关系"开展地质研究、找矿靶区定量优选的可行性和必要性。更多还原

【Abstract】 Today,with rapid development of computer science and technology,ever more geologists are learning and applying big data based research methods,as it has become evident that many geological problems can be solved or clarified by analyzing correlativity of geological data.Here,we used multivariant statistical analysis method to mine the 1∶200000 scale geochemical survey data of stream sediments from western Qinling district,and established a series of quantitative optimization models of target areas for regional Au prospecting.These series of optimization models increased the accuracy for Au metallogenetic prediction in the study area(up to 30% Au prediction accuracy in randomly selected areas);meanwhile,they predicted the elemental composition of Au ore with high agreement with the theoretical value based on Au metallogenic geochemistry.We quantitatively evaluated each element according to its predicting power in all predication models to provide quantitative basis for further research on the genesis and controlling factors of Au ore deposit.Our results demonstrate that massive geological data possess great research potential which can only be exploited by applying big data and quantitative analytical methodologies.At the same time,it fully proved that the feasibility and necessity of quantitative optimization selection of geological research and exploration target area is realized by "identifying the relationship between data and replacing the causal relationship between things".更多还原