Application of improved and efficient image repair algorithm in rock damage experimental research
Abstract 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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-06-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-65790-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585715876102144 |
|---|---|
| author | Mingzhe Xu Xianyin Qi Diandong Geng |
| author_facet | Mingzhe Xu Xianyin Qi Diandong Geng |
| author_sort | Mingzhe Xu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-714617fe35ef4b54890ce70dbc28101d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-714617fe35ef4b54890ce70dbc28101d2025-01-26T12:35:08ZengNature PortfolioScientific Reports2045-23222024-06-0114112710.1038/s41598-024-65790-yApplication of improved and efficient image repair algorithm in rock damage experimental researchMingzhe Xu0Xianyin Qi1Diandong Geng2School of Urban Construction, Yangtze UniversitySchool of Urban Construction, Yangtze UniversitySchool of Urban Construction, Yangtze UniversityAbstract 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.https://doi.org/10.1038/s41598-024-65790-yDigital imageImage restorationTransformer algorithmNeural networkRock damage |
| spellingShingle | Mingzhe Xu Xianyin Qi Diandong Geng Application of improved and efficient image repair algorithm in rock damage experimental research Scientific Reports Digital image Image restoration Transformer algorithm Neural network Rock damage |
| title | Application of improved and efficient image repair algorithm in rock damage experimental research |
| title_full | Application of improved and efficient image repair algorithm in rock damage experimental research |
| title_fullStr | Application of improved and efficient image repair algorithm in rock damage experimental research |
| title_full_unstemmed | Application of improved and efficient image repair algorithm in rock damage experimental research |
| title_short | Application of improved and efficient image repair algorithm in rock damage experimental research |
| title_sort | application of improved and efficient image repair algorithm in rock damage experimental research |
| topic | Digital image Image restoration Transformer algorithm Neural network Rock damage |
| url | https://doi.org/10.1038/s41598-024-65790-y |
| work_keys_str_mv | AT mingzhexu applicationofimprovedandefficientimagerepairalgorithminrockdamageexperimentalresearch AT xianyinqi applicationofimprovedandefficientimagerepairalgorithminrockdamageexperimentalresearch AT diandonggeng applicationofimprovedandefficientimagerepairalgorithminrockdamageexperimentalresearch |