Real-time switching and visualization of logging attributes based on subspace learning
文献类型：期刊论文
| 作者 | Shi, Min; Wu, Zirui; Wang, Suqin; Zhu, Dengming |
| 刊名 | COMPUTERS & GEOSCIENCES
 |
| 出版日期 | 2021 |
| 卷号 | 146页码:11 |
| 关键词 | Geological volume data Visualization Data compression Subspace learning Data exchange |
| ISSN号 | 0098-3004 |
| DOI | 10.1016/j.cageo.2020.104624 |
| 英文摘要 | In three-dimensional visualization, sufficient memory and computing power can ensure real-time graphics rendering. However, due to equipment and algorithm performance limitations, it is difficult to graphically present big volume data efficiently and accurately, especially for high-dimensional and large volume geological data. In this paper we propose a real-time visualization method for logging data, which combines volume data compression and fast switching algorithm. First, we introduce an adaptive sampling method for large volume of data compression. Each block of the same size is sampled according to the dispersion and the sampling density grade, after which ray casting algorithm is used to render compressed volume data. Second, aiming at the graphic presentation delay caused by the exchange of large amounts of data in internal and external memory, a fast switching algorithm(FSA) based on subspace learning is presented. The attributes with strong correlation are put into the same group, from which feature subspace are learned and a mapping model between associated attributes is established according to base vector invariance. Once we need to switch from the currently displayed attribute to another for display, only a few coefficient values in the mapping model need to be changed, reducing the amount of data exchange. Our proposed method can greatly increase the compression ratio and reduce the computing time, ensuring real-time visualization for geological data. |
| 资助项目 | CNPC Logging[2017ZX05019005] ; Institute of Computing Technology, Chinese Academy of Sciences[61972379] ; Institute of Hydrobiology, Chinese Academy of Sciences[YJKYYQ20190055] |
| WOS研究方向 | Computer Science ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000599845500002 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16486]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Shi, Min |
| 作者单位 | North China Elect Power Univ, Chinese Acad Sci, Inst Comp Technol, Sch Control & Comp Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Min,Wu, Zirui,Wang, Suqin,et al. Real-time switching and visualization of logging attributes based on subspace learning[J]. COMPUTERS & GEOSCIENCES,2021,146:11. |
| APA | Shi, Min,Wu, Zirui,Wang, Suqin,&Zhu, Dengming.(2021).Real-time switching and visualization of logging attributes based on subspace learning.COMPUTERS & GEOSCIENCES,146,11. |
| MLA | Shi, Min,et al."Real-time switching and visualization of logging attributes based on subspace learning".COMPUTERS & GEOSCIENCES 146(2021):11. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。