聂虹, 朱月琴, 常力恒, 闫东. 数据驱动下的矿产预测模型构建方法研究[J]. 中国矿业, 2018, 27(9): 82-87. DOI: 10.12075/j.issn.1004-4051.2018.09.033
    引用本文: 聂虹, 朱月琴, 常力恒, 闫东. 数据驱动下的矿产预测模型构建方法研究[J]. 中国矿业, 2018, 27(9): 82-87. DOI: 10.12075/j.issn.1004-4051.2018.09.033
    NIE Hong, ZHU Yueqin, CHANG Liheng, YAN Dong. Research on construction method of data-driven minerals prediction model[J]. CHINA MINING MAGAZINE, 2018, 27(9): 82-87. DOI: 10.12075/j.issn.1004-4051.2018.09.033
    Citation: NIE Hong, ZHU Yueqin, CHANG Liheng, YAN Dong. Research on construction method of data-driven minerals prediction model[J]. CHINA MINING MAGAZINE, 2018, 27(9): 82-87. DOI: 10.12075/j.issn.1004-4051.2018.09.033

    数据驱动下的矿产预测模型构建方法研究

    Research on construction method of data-driven minerals prediction model

    • 摘要: 这是一个计算无处不在、软件定义一切、数据驱动发展的新时代。在矿产预测中,相较于以前统计方法,机器学习、深度学习算法的优势在于能更好地表现出矿化点和空间要素之间的非线性的复杂关系。本文将地质、物探、化探、遥感资料融合在一起,用决策树、支持向量机、卷积神经网络三种算法建模,开展综合信息的矿产预测工作。针对甘肃省北山地区的样本数据,发现相对于卷积神经网络的建模方法,决策树和支持向量机的建模方法更为合适。

       

      Abstract: This is a new era of computing everywhere, software definition and data driven development.In the mineral prediction, compared with previous statistical methods, the advantage of the machine learning and deep learning algorithm is that it can be better to show the complex nonlinear relationship between the mineralized point and the spatial factors.This paper combines geology, geophysical exploration, geochemical exploration and remote sensing data, and uses three algorithms which are decision tree, support vector machine and convolution neural network to carry out mineral prediction work of comprehensive information.According to the sample data of Beishan area in Gansu province, it is found that the modeling method of decision tree and support vector machine is more suitable than that of convolution neural network.

       

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