| 基于数据科学的矿产资源定量预测的理论与方法探索 |
| |
| 引用本文： | 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. |
| |
| 作者姓名： | 左仁广 |
| |
| 作者单位： | 中国地质大学(武汉)地质过程与矿产资源国家重点实验室,湖北武汉430074 |
| |
| 基金项目： | 国家优秀青年科学基金项目(41522206) |
| |
| 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。
|
| 关 键 词： | 数据科学 矿产资源定量预测 地质找矿大数据 机器学习 不确定性 |
| 收稿时间： | 2021-01-10 |
Data science-based theory and method of quantitative prediction of mineral resources |
| |
| ZUO Renguang.Data science-based theory and method of quantitative prediction of mineral resources[J].Earth Science Frontiers,2021,28(3):49-55. |
| |
| Authors: | ZUO Renguang |
| |
| Institution: | State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences(Wuhan), Wuhan 430074, China |
| |
| Abstract: | Quantitative prediction of mineral resources needs the support of data science urgently as the field has now changed from qualitative to quantitative, from data sparse to data intensive. On the basis of previous studies, this paper discusses data science-based theory and method of quantitative prediction of mineral resources. The theoretical basis of such theory and method are correlation theory and anomaly theory. The former, via supervised machine learning algorithms, provides a theoretical basis for the prediction of undiscovered mineral deposits by mining the correlations between geological prospecting big data and locations of mineral deposits; the latter, by detecting geological anomaly present in geological prospecting big data, provides a theoretical basis for the prediction of mineral deposits. This data science-based approach emphasizes the importance of geological prospecting big data and machine learning algorithms, as the type, diversity, quality and accuracy of geospatial data can affect the final prediction results, whilst machine learning algorithms can improve the efficiency of feature extraction and information integration fusion. This paper presents the workflow of quantitative prediction of mineral resources by the data science-based theory and method, introduces the methods for feature extraction and prospecting information fusion, and discusses potential prediction uncertainty inherent in such theory and method. |
| |
| Keywords: | data science quantitative prediction of mineral resources geological prospecting big data machine learning uncertainty |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《地学前缘》浏览原始摘要信息 |
| 点击此处可从《地学前缘》下载免费的PDF全文 |
|
