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机器学习方法在矿产资源定量预测应用研究进展

马瑶 赵江南

马瑶, 赵江南. 机器学习方法在矿产资源定量预测应用研究进展[J]. 地质科技通报, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108
引用本文: 马瑶, 赵江南. 机器学习方法在矿产资源定量预测应用研究进展[J]. 地质科技通报, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108
Ma Yao, Zhao Jiangnan. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108
Citation: Ma Yao, Zhao Jiangnan. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 132-141. doi: 10.19509/j.cnki.dzkq.2021.0108

机器学习方法在矿产资源定量预测应用研究进展

doi: 10.19509/j.cnki.dzkq.2021.0108
基金项目: 

国家重点研发计划 2016YFC0600509

详细信息
    作者简介:

    马瑶(1995-), 女, 现正攻读地质工程专业硕士学位, 主要从事资源信息综合处理方向的研究工作。E-mail:mayaoy2019@163.com

    通讯作者:

    赵江南(1984-), 男, 讲师, 主要从事矿产资源定量预测方面的研究工作。E-mail:zhaojn@cug.edu.cn

  • 中图分类号: P624.7

Advances in the application of machine learning methods in mineral prospectivity mapping

  • 摘要: 回顾了国内外在矿产资源定量预测研究领域的发展历程,对近十年来国外相关方向的文献进行了统计对比分析,结果显示机器学习方法已经成为矿产资源定量预测研究领域的热点方向,并主要在如下3个方面发挥了积极的作用:①提取和挖掘复杂数据中隐藏的难以识别的矿化信息;②致矿异常信息关联与转换;③多源地学数据的致矿异常信息融合、预测和发现矿床。对逻辑回归、人工神经网络、随机森林与支持向量机等主要机器学习算法与模型在矿产资源定量预测实践中的应用效果进行了评述,并探讨了在实际应用过程中存在的样本选择、错分代价、不确定性评价以及模型性能评价等主要问题及目前的解决方案。最后提出基于大数据与机器学习的矿产资源定量预测是未来发展的重要趋势。

     

  • 图 1  多元统计方法与机器学习方法挖掘地球化学致矿异常信息对比图解[24]

    Figure 1.  Comparison between multivariate statistical methods and machine learning methods in geochemical anomaly identification

    图 2  闽西北火山岩型铀矿成矿有利度图

    Figure 2.  Probability map of volcanic type uranium deposits in northwestern Fujian district obtained by ANN (a) and Wof E (b)

    图 3  2种方法预测结果的ROC曲线

    Figure 3.  ROC curvesof the two methods

    表  1  矿产资源定量预测模型分类

    Table  1.   Mineral prospectivity mapping predication models

    类型 模型参数 实例
    数据驱动 统计矿化指标与已知矿床的关联 逻辑回归、证据权
    人工神经网络、随机森林
    证据信念函数、支持向量机
    似然比分析、判别分析
    有利度分析、贝叶斯网络分类
    知识驱动 由专家评估 布尔逻辑、标志叠加模型
    模糊逻辑、证据信念函数
    Dempster-Shafer理论
    下载: 导出CSV

    表  2  机器学习不同类别划分[26]

    Table  2.   Categories of machine learning

    类别 特征 评价 优化 代表算法
    符号主义 使用符号、规则和逻辑来表征知识和进行逻辑推理 准确度 逆向演绎 决策树
    联结主义 使用概率矩阵和加权神经元来动态地识别和归纳模式 平方误差 梯度下降 神经网络
    进化主义 生成变化,为特定目标获取最优 适应度 遗传搜索 遗传算法
    贝叶斯主义 获取发生的可能性来进行概率推理 后验概率 概率推理 朴素贝叶斯、马尔科夫
    类推主义 根据约束条件优化函数 间距 约束优化 支持向量机
    下载: 导出CSV
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