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基于加权信息量法的找矿靶区优选方法 ——以青海省都兰县沟里地区金矿为例
- 作者：
牟江涛|吉明甲|魏丽琼|熊浩宇|薛林福
- 作者单位：
- （1. 吉林大学地球科学学院；2. 青海省地质调查局）
基金项目：
- 青海省地质勘查专项资金项目（2023085031ky006）
详细信息：
- 作者简介：
- 牟江涛（2000—），男，硕士研究生，研究方向为人工智能找矿预测等；E⁃mail：2322553534＠qq. com
- 通讯作者：
- 薛林福（1962—），男，教授，博士，从事地学大数据分析、三维地质建模与地质过程模拟等方面的研究工作；E⁃mail：xuelf@jlu. edu. cn
- PDF下载
Optimized selection of prospecting target areas based on the weighted information value method—A case study of gold deposits in the Gouli Area, Dulan County, Qinghai Province
- English Author：
Mou Jiangtao¹, Ji Mingjia², Wei Liqiong², Xiong Haoyu¹, Xue Linfu¹
- Unit：
- （1. College of Earth Sciences, Jilin University; 2. Qinghai Geological Survey）
- 摘要
- 在线预览
- 参考文献
摘要:
找矿靶区优选是矿产勘查的重要环节，是连接成矿预测与勘查工作的重要组成部分。然而，传统找矿靶区筛选方法以综合找矿模型的相似类比为主，主要采用定性分析方式，针对同一地质现象或地质体，不同专家侧重点也不尽相同。为减少研究结果的多解性和不确定性，通过采用加权信息量法实现找矿靶区优选的定量分析，科学、客观地圈定找矿靶区。在沟里地区已经开展的金矿成矿预测基础上，根据实际地质特征，选取断裂影响强度、地质界面影响强度、Au元素异常、磁异常、断裂空间聚类、地质界面空间聚类等 6个地质特征作为评价因子，对生成的 69个预测区进行筛选排序，最终圈定2个可供进一步勘查的找矿靶区，这2个找矿靶区均具有良好的金矿找矿远景，对后续勘查工作具有一定的指导意义。
关键词:
矿产勘查；金矿；加权信息量法；评价因子；沟里地区；找矿靶区；定量分析
Abstract:
The optimized selection of prospecting target areas is a critical step in mineral exploration, serving as a key link between metallogenic prediction and practical exploration. Traditional methods for target areas screening rely primarily on qualitative similarity comparisons using integrated prospecting models, which often lead to interpretational variability among experts when evaluating the same geological phenomena or units. To reduce ambiguity and uncertainty in results, this study introduces the weighted information value method to achieve quantitative analysis for optimized selection of prospecting target areas, enabling scientific and objective delineation of prospecting target areas. Buildingon existing metallogenic predictions for gold deposits in the Gouli Area, 6 geological factors were selected as evaluation criteria based on actual geological characteristics: fracture influence intensity, geological interface influence intensity. Au geochemical anomalies, magnetic anomalies, fracture spatial clustering, and geological interface spatial clustering. A total of 69 prediction grids generated were ranked and filtered, ultimately identifying 2 prioritized prospecting target areas with significant gold mineralization potential. These target areas provide guidance for subsequent exploration efforts.
Keywords:
mineral resources prospecting; gold deposits; weighted information value method; evaluation factors; Gouli Area; prospecting target areas; quantitative analysis
