Application of seismic multiattribute machine learning to determine coal strata thickness
文献类型：期刊论文
| 作者 | Wu, Yanhui2; Wang, Wei1; Zhu, Guowei2; Wang, Peng3 |
| 刊名 | JOURNAL OF GEOPHYSICS AND ENGINEERING
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 18期号:6页码:834-844 |
| 关键词 | seismic multiattribute optimal multiparameter coal strata thickness machine learning back propagation artificial neural network |
| ISSN号 | 1742-2132 |
| DOI | 10.1093/jge/gxab054 |
| 通讯作者 | Wang, Wei(wang_wei@lreis.ac.cn) |
| 英文摘要 | The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas. |
| WOS关键词 | FACIES INTERPRETATION ; NEURAL-NETWORKS ; ATTRIBUTES ; INVERSION ; THIN |
| 资助项目 | project 'Subject of National Key R&D Program of China[2018YFC0807800] ; project 'Subject of National Key R&D Program of China[41641040] |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000765572000001 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | project 'Subject of National Key R&D Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171932]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Wei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.China Univ Mining & Technol, Sch Geosci & Surveying Engn, State Key Lab Coal Resources & Safe Mining, Beijing 100083, Peoples R China 3.Hebei Coal Res Inst Co LTD, Xingtai 054000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Yanhui,Wang, Wei,Zhu, Guowei,et al. Application of seismic multiattribute machine learning to determine coal strata thickness[J]. JOURNAL OF GEOPHYSICS AND ENGINEERING,2021,18(6):834-844. |
| APA | Wu, Yanhui,Wang, Wei,Zhu, Guowei,&Wang, Peng.(2021).Application of seismic multiattribute machine learning to determine coal strata thickness.JOURNAL OF GEOPHYSICS AND ENGINEERING,18(6),834-844. |
| MLA | Wu, Yanhui,et al."Application of seismic multiattribute machine learning to determine coal strata thickness".JOURNAL OF GEOPHYSICS AND ENGINEERING 18.6(2021):834-844. |
入库方式： OAI收割
来源：地理科学与资源研究所
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