中国科学院地质与地球物理研究所机构知识库(IGGCAS OpenIR): Scalable double regularization for 3D Nano 您所在的位置:网站首页 emerged用法 中国科学院地质与地球物理研究所机构知识库(IGGCAS OpenIR): Scalable double regularization for 3D Nano

中国科学院地质与地球物理研究所机构知识库(IGGCAS OpenIR): Scalable double regularization for 3D Nano

2023-03-27 06:57| 来源: 网络整理| 查看: 265

IGGCAS OpenIR  > 其他部门 开始提交 已提交作品 待认领作品 已认领作品 未提交全文 收藏管理 QQ客服 官方微博 反馈留言 Scalable double regularization for 3D Nano-CT reconstruction Tang, Wei1; Li, Meng2 2020-09-01 发表期刊JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING ISSN0920-4105 卷号192页码:9摘要Nano-CT (computerized tomography) has emerged as a non-destructive high-resolution cross-sectional imaging technique to effectively study the sub-mu m pore structure of shale, which is of fundamental importance to the evaluation and development of shale oil and gas. Nano-CT poses unique challenges to the inverse problem of reconstructing the 3D structure due to the lower signal-to-noise ratio (than Micro-CT) at the nano-scale, increased sensitivity to the misaligned geometry caused by the movement of object manipulator, limited sample size, and a larger volume of data at higher resolution. We propose a scalable double regularization (SDR) method to utilize the entire dataset for simultaneous 3D structural reconstruction across slices through total variation regularization within slices and L-1 regularization between adjacent slices. SDR allows information borrowing both within and between slices, contrasting with the traditional methods that usually build on slice by slice reconstruction. We develop a scalable and memory-efficient algorithm by exploiting the systematic sparsity and consistent geometry induced by such Nano-CT data. We illustrate the proposed method using synthetic data and two Nano-CT imaging datasets of Jiulaodong (JLD) shale and Longmaxi (LMX) shale acquired in the Sichuan Basin. These numerical experiments show that the proposed method substantially outperforms selected alternatives both visually and quantitatively. 关键词Shale Nano-CT Image reconstruction Regularization Scalable algorithm DOI10.1016/j.petrol.2020.107271 资助者China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health 关键词[WOS]CONVOLUTIONAL NEURAL-NETWORK ; X-RAY CT ; IMAGE-RECONSTRUCTION ; ALGORITHMS ; QUALITY ; SHALE ; ART 语种英语 资助项目China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB1002010] ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health[1R24MH117529] 资助者China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; ORAU Ralph E. Powe Junior Faculty Enhancement Award ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health ; BRAIN Initiative of the United States National Institutes of Health WOS研究方向Energy & Fuels ; Engineering WOS类目Energy & Fuels ; Engineering, Petroleum WOS记录号WOS:000575129700052 出版者ELSEVIER 引用统计 文献类型期刊论文 条目标识符http://ir.iggcas.ac.cn/handle/132A11/98304 专题其他部门 通讯作者Li, Meng作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Beijing, Peoples R China2.Rice Univ, Dept Stat, Houston, TX 77251 USA 第一作者单位中国科学院地质与地球物理研究所 推荐引用方式GB/T 7714 Tang, Wei,Li, Meng. Scalable double regularization for 3D Nano-CT reconstruction[J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,2020,192:9. APA Tang, Wei,&Li, Meng.(2020).Scalable double regularization for 3D Nano-CT reconstruction.JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,192,9. MLA Tang, Wei,et al."Scalable double regularization for 3D Nano-CT reconstruction".JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 192(2020):9. 条目包含的文件 条目无相关文件。 个性服务推荐该条目保存到收藏夹查看访问统计导出为Endnote文件谷歌学术谷歌学术中相似的文章[Tang, Wei]的文章[Li, Meng]的文章百度学术百度学术中相似的文章[Tang, Wei]的文章[Li, Meng]的文章必应学术必应学术中相似的文章[Tang, Wei]的文章[Li, Meng]的文章相关权益政策暂无数据收藏/分享 所有评论 (0) [发表评论/异议/意见] 暂无评论   评论 权益异议 反馈意见 评注功能仅针对注册用户开放,请您登录 修改评论 取消 确定

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