{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:18:43Z","timestamp":1780611523141,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52208330"],"award-info":[{"award-number":["52208330"]}]},{"name":"National Natural Science Foundation of China","award":["42177168"],"award-info":[{"award-number":["42177168"]}]},{"name":"National Natural Science Foundation of China","award":["2022JJ40500"],"award-info":[{"award-number":["2022JJ40500"]}]},{"name":"National Natural Science Foundation of China","award":["21KB13"],"award-info":[{"award-number":["21KB13"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["52208330"],"award-info":[{"award-number":["52208330"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["42177168"],"award-info":[{"award-number":["42177168"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ40500"],"award-info":[{"award-number":["2022JJ40500"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["21KB13"],"award-info":[{"award-number":["21KB13"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Changsha University of Science and Technology","award":["52208330"],"award-info":[{"award-number":["52208330"]}]},{"name":"Changsha University of Science and Technology","award":["42177168"],"award-info":[{"award-number":["42177168"]}]},{"name":"Changsha University of Science and Technology","award":["2022JJ40500"],"award-info":[{"award-number":["2022JJ40500"]}]},{"name":"Changsha University of Science and Technology","award":["21KB13"],"award-info":[{"award-number":["21KB13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs16081310","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T08:55:12Z","timestamp":1712652912000},"page":"1310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4336-5275","authenticated-orcid":false,"given":"Hong","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9231-0732","authenticated-orcid":false,"given":"Guangliang","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiqi","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MSP.2019.2909433","article-title":"The tomographic approach to ground-penetrating radar for underground exploration and monitoring: A more user-friendly and unconventional method for subsurface investigation","volume":"36","author":"Ambrosanio","year":"2019","journal-title":"IEEE Signal Process. 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