Application of improved and efficient image repair algorithm in rock damage experimental research
文献类型：期刊论文
| 作者 | Xu, Mingzhe2; Qi, Xianyin1,2; Geng, Diandong2 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2024-06-27 |
| 卷号 | 14期号:1页码:27 |
| 关键词 | Digital image Image restoration Transformer algorithm Neural network Rock damage |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-024-65790-y |
| 英文摘要 | In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. |
| 资助项目 | The Natural Science Foundation of Hubei Province[2020CFB367] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001258394400025 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/41833]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Qi, Xianyin |
| 作者单位 | 1.Chinese Acad Sci, Wuhan Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China 2.Yangtze Univ, Sch Urban Construct, Jingzhou 434023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Mingzhe,Qi, Xianyin,Geng, Diandong. Application of improved and efficient image repair algorithm in rock damage experimental research[J]. SCIENTIFIC REPORTS,2024,14(1):27. |
| APA | Xu, Mingzhe,Qi, Xianyin,&Geng, Diandong.(2024).Application of improved and efficient image repair algorithm in rock damage experimental research.SCIENTIFIC REPORTS,14(1),27. |
| MLA | Xu, Mingzhe,et al."Application of improved and efficient image repair algorithm in rock damage experimental research".SCIENTIFIC REPORTS 14.1(2024):27. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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。