地学前缘 ›› 2021, Vol. 28 ›› Issue (3): 49-55.DOI: 10.13745/j.esf.sf.2020.12.1
收稿日期:
2021-01-10
修回日期:
2021-03-20
出版日期:
2021-05-20
发布日期:
2021-05-23
作者简介:
左仁广(1981—),男,博士,教授,博士生导师,主要从事数学地质与矿产勘查方面的研究。E-mail: zrguang@cug.edu.cn
基金资助:
Received:
2021-01-10
Revised:
2021-03-20
Online:
2021-05-20
Published:
2021-05-23
摘要:
矿产资源预测已从定性走向了定量,从数据稀疏型走向了数据密集型,亟须数据科学支撑。本文在前人研究基础上,讨论了基于数据科学的矿产资源定量预测理论与方法,该方法的理论基础为相关性理论与异常理论,前者采用监督的机器学习方法挖掘地质找矿大数据与矿床的相关性为预测未发现矿床提供了理论基础;后者采用非监督的机器学习方法识别地质找矿大数据蕴含的地质异常为预测矿床提供了理论依据。该理论与方法强调地质找矿大数据和机器学习的重要性,其中,数据种类的多样性及数据精度和质量会影响预测结果的好坏,机器学习可提高特征提取与信息集成融合效率。此外,本文讨论了基于数据科学的矿产资源定量预测理论与方法的技术框架、特征提取、数据集成融合方法,以及该理论与方法引入的不确定性。
中图分类号:
左仁广. 基于数据科学的矿产资源定量预测的理论与方法探索[J]. 地学前缘, 2021, 28(3): 49-55.
ZUO Renguang. Data science-based theory and method of quantitative prediction of mineral resources[J]. Earth Science Frontiers, 2021, 28(3): 49-55.
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