Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China
文献类型：期刊论文
| 作者 | Zhang, Nannan; Zhou, Kefa; Li, Dong |
| 刊名 | EARTH SCIENCE INFORMATICS
 |
| 出版日期 | 2018-12 |
| 卷号 | 11期号:4页码:553-566 |
| ISSN号 | 1865-0473 |
| 关键词 | Mineral prospectivity mapping Back propagation neural network Support vector machines Weight of evidence |
| DOI | 10.1007/s12145-018-0346-6 |
| 产权排序 | [Zhang, Nannan ; Zhou, Kefa] Chinese Acad Sci, State Key Lab Desert & Oasis Ecol, Xinjiang Inst Ecol & Geog, Urumqi 830011, Xinjiang, Peoples R China ; [Zhang, Nannan ; Zhou, Kefa] Chinese Acad Sci, Xinjiang Res Ctr Mineral Resources, Xinjiang Inst Ecol & Geog, Urumqi 830011, Xinjiang, Peoples R China ; [Zhang, Nannan ; Zhou, Kefa] Xinjiang Key Lab Mineral Resources & Digital Geog, Urumqi 830011, Xinjiang, Peoples R China ; [Li, Dong] Chinese Acad Sci, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China ; [Li, Dong] Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 文献子类 | Article |
| 英文摘要 | Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9-10-1; for the SVM model, a radial basis function was selected as the kernel function with best C=1 and= 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area. |
| WOS关键词 | KNOWLEDGE-DRIVEN METHOD ; GEOCHEMICAL DATA ; COPPER EXPLORATION ; GEOPHYSICAL-DATA ; FUZZY-LOGIC ; INTEGRATION ; REGRESSION ; AREA ; IRAN ; ROC |
| WOS研究方向 | Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary |
| 语种 | 英语 |
| WOS记录号 | WOS:000449959500005 |
| 资助机构 | National Natural Science Foundation of China [41602339, U1503291] ; Western Light Foundation of the Chinese Academy of Sciences (CAS) [XBBS-2014-19] ; Xinjiang Uygur Autonomous Major Project [201330121-3] ; National Basic Research Program of China [2014CB440803] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030204] |
| 源URL | [http://ir.yic.ac.cn/handle/133337/24589]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Desert & Oasis Ecol, Xinjiang Inst Ecol & Geog, Urumqi 830011, Xinjiang, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Xinjiang Res Ctr Mineral Resources, Xinjiang Inst Ecol & Geog, Urumqi 830011, Xinjiang, Peoples R China; 4.Xinjiang Key Lab Mineral Resources & Digital Geog, Urumqi 830011, Xinjiang, Peoples R China; 5.Chinese Acad Sci, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai Inst Coastal Zone Res, Yantai 264003, Shandong, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Nannan,Zhou, Kefa,Li, Dong. Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China[J]. EARTH SCIENCE INFORMATICS,2018,11(4):553-566. |
| APA | Zhang, Nannan,Zhou, Kefa,&Li, Dong.(2018).Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China.EARTH SCIENCE INFORMATICS,11(4),553-566. |
| MLA | Zhang, Nannan,et al."Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China".EARTH SCIENCE INFORMATICS 11.4(2018):553-566. |
入库方式： OAI收割
来源：烟台海岸带研究所
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