Advances and prospects of big data and mathematical geoscience
文献类型：期刊论文
| 作者 | Zhou YongZhang1,2,3; Chen Shuo1,2,3; Zhang Qi4; Xiao Fan1,2,3; Wang ShuGong1,2,3; Liu YanPeng1,2,3; Jiao ShouTao1,2,3 |
| 刊名 | ACTA PETROLOGICA SINICA
 |
| 出版日期 | 2018 |
| 卷号 | 34期号:2页码:255-263 |
| ISSN号 | 1000-0569 |
| 关键词 | Big Data Mining Dimensionality Reduction Graph Data Processing Infinite Data Stream Machine Learning |
| 文献子类 | Article |
| 英文摘要 | Dimensionality reduction, graph data processing, stream data mining, machine learning, association rule algorithm and recommendation system are included in the core technologies of big data and mathematical geoscience. Intelligent geology, including construction of big data-based intelligent metallogenetic and prospecting models, is a highly valuable research direction. Dimensionality reduction aims at extracting low dimensional feature sets out of initial high dimensional feature ones, which can effectively eliminate irrelevant and redundant features, and enhancing the comprehensibility of learning results. Hash algorithm, clustering and PCA are frequently used as tool of dimensionality reduction. Machine learning is the core of artificial intelligence and the fundamental way to endow computer with intelligence. Unity for machine learning and artificial intelligence is emerging. The training model of deep learning often needs huge amounts of data, leading to the raising attention of transfer learning. Graph pattern recognition is an important technology of data mining. Community structure identification has great value to understand the structure and function of the entire network. It can help analyze and predict the interaction between different elements in the network. Immersive virtual reality (VR) technology is another important direction to achieve the visualization of big data. It is of special value in demonstrating mineral resource exploration data characterized by multivariate, heterogeneous, time-spatial, nonlinear, and multi-scale features. Utilizing VR technology to visualize geology and mineral data can result in new insight into mineral exploration under the background of big data era. Infinite data streams widely exist, and even may be automatically and continuously generated in many geological, geochemical, and geophysical monitoring projects. Point query, range query, inner product query, quantile calculation, frequent item-set computing and the like are included in data stream mining. Association rules and recommendation systems, as essential algorithms in data mining, are seeing an expanding application scope. Bayes theorem has unique value in the era of big data. The Bayesian Network is a revolutionary tool for genesis modelling. Intelligent Geology (IG) is still at its primary stage. The construction of big data-based intelligent metallogenetic and mineral prospecting models is part of IG. The revolution of research mode of the metallogenetic and mineral prospecting model will emerge with the worldwide participation of teams together with the help of intemet and cloud computing technologies. |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| 出版者 | SCIENCE PRESS |
| WOS记录号 | WOS:000429429000001 |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/88325]  |
| 专题 | 地质与地球物理研究所_离退休科研人员 |
| 通讯作者 | Zhou YongZhang |
| 作者单位 | 1.Sun Yat Sen Univ, Ctr Earth Environm & Resources, Guangzhou 510275, Guangdong, Peoples R China 2.Guangdong Prov Key Lab Mineral Resource & Geol Pr, Guangzhou 510275, Guangdong, Peoples R China 3.Sun Yat Sen Univ, Sch Earth Sci & Engn, Guangzhou 510275, Guangdong, Peoples R China 4.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou YongZhang,Chen Shuo,Zhang Qi,et al. Advances and prospects of big data and mathematical geoscience[J]. ACTA PETROLOGICA SINICA,2018,34(2):255-263. |
| APA | Zhou YongZhang.,Chen Shuo.,Zhang Qi.,Xiao Fan.,Wang ShuGong.,...&Jiao ShouTao.(2018).Advances and prospects of big data and mathematical geoscience.ACTA PETROLOGICA SINICA,34(2),255-263. |
| MLA | Zhou YongZhang,et al."Advances and prospects of big data and mathematical geoscience".ACTA PETROLOGICA SINICA 34.2(2018):255-263. |
入库方式： OAI收割
来源：地质与地球物理研究所
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