| 基于深度学习的钨钼找矿靶区预测方法研究 |
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| 引用本文： | 蔡惠慧,朱伟,李孜轩,刘园园,李龙斌,刘畅.基于深度学习的钨钼找矿靶区预测方法研究[J].地球信息科学,2019,21(6):928-936. |
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| 作者姓名： | 蔡惠慧 朱伟 李孜轩 刘园园 李龙斌 刘畅 |
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| 作者单位： | 1. 中国地质大学（北京）,北京 1000832. 中国地质调查局发展研究中心,北京1000373. 陕西省矿产地质调查中心,西安 7100684. 中国地质大学（武汉）信息工程学院,武汉 4300745. 中国地质大学（武汉）国家地理信息系统工程技术研究中心,武汉 430074 |
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| 基金项目： | 智能地质调查系统开发与推广（DD20160355） |
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| 摘 要： | 随着矿产勘查工作由浅部矿向深部隐伏矿、由易识别矿向难识别矿发展,找矿难度日益增大,地质专家越来越重视新理论、新方法、新技术的应用。深度学习作为人工智能的前沿领域/技术,对于实现矿产资源预测“智能化预测评价”具有得天独厚的优势。本文以陕西省镇安县西部钨钼矿集区单元素化探异常原始数据为基础,提出了基于深度学习的钨钼矿产评价方法。该方法以归一化地球化学数据作为模型训练数据,通过深度学习中深度自编码网络方法实现异常值提取进而识别重点成矿有利地段,实现矿产资源找矿远景区定性预测。研究结果表明,在对957条单元素化探异常原始数据分类且做好模型标签后,整个过程在计算机的“黑盒子”中自动完成学习和预测,相较于传统预测研究方法,本文方法具有自动化程度高和客观性强的特征。此外,本文利用已知矿点构建训练数据集,采用随机森林方法对预测区进行矿产资源找矿靶区预测圈定,为进一步缩小找矿靶区范围提供科学依据。
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| 关 键 词： | 随机森林方法 深度学习 钨钼多金属矿产资源 大数据 预测 评价 陕西镇安西部 |
| 收稿时间： | 2019-01-18 |
Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning |
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| Huihui CAI,Wei ZHU,Zixuan LI,Yuanyuan LIU,Longbin LI,Chang LIU.Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning[J].Geo-information Science,2019,21(6):928-936. |
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| Authors: | Huihui CAI Wei ZHU Zixuan LI Yuanyuan LIU Longbin LI Chang LIU |
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| Institution: | 1. China University of Geosciences (Beijing),Beijing 100083, China2. Development and Research Center of China Geological Survey, Beijing 100037, China3. Shanxi Center of Mineral Geological Survey, Xi'an 710068, China4. Department of Information Engineering, China University of Geosciences, Wuhan 430074, China5. National Engineering Research Center of Geographic Information System, Wuhan 430074, China; |
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| Abstract: | With the exploration of minerals from shallow mines to deep concealed mines, from easy-to-identify mines to difficult-to-identify mines, the difficulty of prospecting is increasing, and geological experts are paying more and more attention to the application of new theories, new methods, and new technologies. As a frontier field and technology of artificial intelligence, deep learning has a unique advantage in realizing the intelligent forecasting and evaluation of mineral resources. The method uses normalized geochemical data as the training data to extract outliers by a neural network called Autoencoder and identify the favorable mineralization areas, and then realizes the qualitative prediction of mineral resources prospecting prospect. The research results show that after classifying the original data of 957 single elements geochemical anomalies and labeling of the model, the whole process automatically completes the learning and prediction in the "black box" of the computer, compared with the traditional prediction research method, this method of research is highly automated and objective. In addition, this paper uses the known mine sites to construct the training dataset, and uses the random forest method to predict the mineral resources prospecting target area in the prediction area, which provides a scientific basis for further narrowing the scope of the prospecting target area. |
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| Keywords: | random forest method deep learning tung-molybdenum polymetallic mineral resources big data prediction evaluation Western Zhenan Shaanxi |
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