Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China
Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China
复制标题
宣城—马姑山地区深部矽卡岩型矿化圈定三维找矿远景模拟
DOI：
10.3390/min12091174
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发表时间：
2022
期刊：
影响因子：
2.5
通讯作者：
Feng Yuan
中科院分区：
文献类型：
--
作者：
Fandong Meng;Xiaohui Li;Yuheng Chen;Rui Ye;Feng Yuan
长江中下游成矿带是我国重要的铜、铁多金属成矿带。今天的经济发展离不开金属矿产资源的支持。随着我国浅部易识别矿山的不断开采，深部隐伏矿山的找矿工作显得尤为重要。矿产远景建模是提高矿产勘查效率的重要手段。随着资源需求的增加和勘探难度的加大，传统的二维DMPM往往难以反映深部矿床信息。开展3DMPM研究需要更多的大型矿床。随着人工智能的兴起，机器学习与地质大数据的结合成为3DMPM领域的热点问题。本文以宣城-马古山地区的实际资料为基础，对三维多点投影法进行了实例研究。两种机器学习方法，随机森林和逻辑回归，被选中进行比较。结果表明，基于随机森林方法的3DMPM比Logistic回归方法具有更好的性能。它能较好地表征地质构造组合与成矿分布的对应关系，在测试集上的准确率达到96.63%。这意味着随机森林模型可以为3DMPM期间集成预测数据提供更有效和准确的支持。最后，在宣城-马孤山地区深部圈定了5个具有良好成矿潜力的找矿靶区，为今后的找矿工作提供了依据。
The Middle–Lower Yangtze River Metallogenic Belt is an important copper and iron polymetallic metallogenic belt in China. Today’s economic development is inseparable from the support of metal mineral resources. With the continuous exploitation of shallow and easily identifiable mines in China, the prospecting work of deep and hidden mines is very important. Mineral prospectivity modeling (MPM) is an important means to improve the efficiency of mineral exploration. With the increase in resource demands and exploration difficulty, the traditional 2DMPM is often difficult to use to reflect the information of deep mineral deposits. More large-scale deposits are needed to carry out 3DMPM research. With the rise of artificial intelligence, the combination of machine learning and geological big data has become a hot issue in the field of 3DMPM. In this paper, a case study of 3DMPM is carried out based on the Xuancheng–Magushan area’s actual data. Two machine learning methods, the random forest and the logistic regression, are selected for comparison. The results show that the 3DMPM based on random forest method performs better than the logistic regression method. It can better characterize the corresponding relationship between the geological structure combination and the metallogenic distribution, and the accuracy in the test set reaches 96.63%. This means that the random forest model could provide more effective and accurate support for integrating predictive data during 3DMPM. Finally, five prospecting targets with good metallogenic potential are delineated in the deep area of the Xuancheng–Magushan area for future exploration.