Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning
文献类型：期刊论文
| 作者 | Xu, Hong1,3; Yan, Jie1,3; Feng, Guangliang2; Jia, Zhuo1,3; Jing, Peiqi1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 16期号:8页码:20 |
| 关键词 | ground-penetrating radar lithological structures rock formations deep learning |
| DOI | 10.3390/rs16081310 |
| 英文摘要 | Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001210500500001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/41232]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Feng, Guangliang |
| 作者单位 | 1.Changsha Univ Sci & Technol, Key Lab Safety Control Bridge Engn, Minist Educ, Changsha 410114, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Hong,Yan, Jie,Feng, Guangliang,et al. Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning[J]. REMOTE SENSING,2024,16(8):20. |
| APA | Xu, Hong,Yan, Jie,Feng, Guangliang,Jia, Zhuo,&Jing, Peiqi.(2024).Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning.REMOTE SENSING,16(8),20. |
| MLA | Xu, Hong,et al."Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning".REMOTE SENSING 16.8(2024):20. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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