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Peng Lin - IEEE Xplore Author Profile
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Peng Lin

Publications
2
Citations
6
Publications by Year
20222025
Co-Authors:
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Peng Lin

Affiliation

Institute of Geotechnical and Underground Engineering and the School of Qilu Transportation
Shandong University
Jinan, Shandong, China

Publication Topics

Biography

Peng Lin received the Ph.D. degree in geotechnical engineering from the Institute of Geotechnical and Underground Engineering, Shandong University, Jinan, China, in 2019.,From 2017 to 2019, he was a joint Ph.D. Student with the School of Environment, The University of Queensland, Brisbane, QLD, Australia. He is currently an Associate Professor with the School of Qilu Transportation, Shandong University. His research interests include the identification of adverse geology, the mechanism of water and mud inrush, risk assessment, and disaster prevention.(Based on document published on 4 April 2025).
Publications
2
Citations
6
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (6)
Author's Published Works

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Four Typical Variation Patterns of Mid-Infrared Spectra of the Felsic Mineral Anomalies for Fault Zone Identification

Tengfei Yu;Tao Han;Peng Lin;Zhenhao Xu;Ruiqi Shao

IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Volume: 63 | Journal Article |
Mid-infrared (IR) spectroscopy has the advantage of being fast and nondestructive for mineral identification and geological analysis. Mineral anomalies in fault zones are geological markers for fault identification. However, fault rock spectra acquired by remote sensing techniques are often a mixture of multiple endmembers. The mid-IR spectral variability of mineral anomaly patterns in fault zones...Show More

Four Typical Variation Patterns of Mid-Infrared Spectra of the Felsic Mineral Anomalies for Fault Zone Identification

Tengfei Yu;Tao Han;Peng Lin;Zhenhao Xu;Ruiqi Shao

Year: 2025 | Volume: 63 | Journal Article |

Intelligent On-Site Lithology Identification Based on Deep Learning of Rock Images and Elemental Data

Zhenhao Xu;Heng Shi;Peng Lin;Wen Ma

IEEE Geoscience and Remote Sensing Letters
Year: 2022 | Volume: 19 | Journal Article |
Cited by: Papers (6)
To reduce the disadvantages of factors such as limited expression of 2-D images, weathering, or man-made pollution on image-based methods and realize rapid and accurate on-site lithology identification, we propose an intelligent lithology identification method based on the deep learning of rock images and element information. The initial lithology probabilities and locations are first determined f...Show More

Intelligent On-Site Lithology Identification Based on Deep Learning of Rock Images and Elemental Data

Zhenhao Xu;Heng Shi;Peng Lin;Wen Ma

Year: 2022 | Volume: 19 | Journal Article |

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