| 基于证据加权模型的综合信息成矿预测 |
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| 引用本文： | 杨佳佳,林楠.基于证据加权模型的综合信息成矿预测[J].地质学报,2016,90(10):2908-2918. |
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| 作者姓名： | 杨佳佳 林楠 |
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| 作者单位： | 1）中国地质调查局沈阳地质调查中心，沈阳，110034；2）吉林建筑工程大学测绘与勘查工程学院，长春，130018 |
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| 基金项目： | 中国地质调查局资助项目 |
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| 摘 要： | 综合信息成矿预测是复杂高维非线性系统的建模与评价过程,通过识别和提取地、物、化、遥等多源地学观测数据中的综合致矿地质异常信息,并以此为依据进行成矿预测。成矿预测是在科学预测理论的指导下,应用地质成矿理论和数理统计方法对地质、物探、化探、遥感等信息进行充分挖掘,剖析成矿地质条件,总结成矿规律,建立综合信息成矿模型并应用于成矿预测,从而圈定和评价成矿远景区,为区域找矿工作部署和矿产资源开发利用的统筹规划提供科学依据。本次研究将证据加权模型引入到成矿预测研究中,通过建立和评价地质信息、化探信息、遥感蚀变信息、遥感线环形构造密度信息与已知矿化点的关系,然后用贝叶斯公式计算成矿后验概率,推出研究区成矿预测结果。研究结果表明:综合信息成矿模型预测结果符合研究区地质成矿规律,和已知成矿点吻合率达71.4%。
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| 关 键 词： | 证据加权 综合信息找矿 成矿预测 遥感地质 成矿远景区 |
| 收稿时间： | 5/3/2016 12:00:00 AM |
| 修稿时间： | 7/4/2016 12:00:00 AM |
Comprehensive Information Prospecting Based on the Weightd Evidence Model |
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| Abstract: | Comprehensive information extraction and interpretation is a complex, high dimensional and non linear process to model and evaluate mineral resources.The main purpose of this process can be divided into two steps. The first step is to identify and extract the mineral anomalies associated with mineralization from multiple sources of geological data. The data adopted here are usually geological, geophysical, geochemical, and remote sensing observation data. Based on these mineral anomalies, the second step is to predict the metallogenic information. Meanwhile,With the guidance of scientific prediction theory, researchers analyze the geological conditions of mineralization, summarize the metallogenic laws and establish the comprehensive models of metallogenic prediction. These models could be used to depict and evaluate the prospecting metallogenic target, and finally provide scientific proof for regional prospecting and mineral resources development. In this project. The weightd evidence model will be applied in metallogenic prediction, which will be used to establish and evaluate the relationship between geological information and the mineralization, geochemical information, remote sensing alteration information, remote sensing linear structure with the known mineralization, then, bayes formula has been used to define the metallogenic posterior probability and delineate the mineralization prospect areas. The results conformed to the conditions of geological and metallogenic characteristics, and it agree with the known mines rate of 71.4%. |
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| Keywords: | weights of evidence comprehensive information prospecting metallogenic prediction remote sensing geological the mineralization prospect areas |
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