In situ, non-destructive and rapid mineral mapping in tunnels with hyperspectral imaging

In situ, non-destructive and rapid mineral mapping in tunnels with hyperspectral imaging

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Shan Li^a^,^b, Peng Lin^a^,^b, Kai Yang^a^,^b, Zhenhao Xu^a^,^b^,^*

Underground Space | 2026, 27 : 301 - 320

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Underground Space | 2026, 27: 301-320

• Research Paper •

In situ, non-destructive and rapid mineral mapping in tunnels with hyperspectral imaging

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Shan Li^a^,^b, Peng Lin^a^,^b, Kai Yang^a^,^b, Zhenhao Xu^a^,^b^,^*

Affiliations

^aState Key Laboratory for Tunnel Engineering, Institute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, China

^bSchool of Qilu Transportation, Shandong University, Jinan 250061, China

Published: 2026-04-10 doi: 10.1016/j.undsp.2025.11.003

Outline

Abstract

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Hyperspectral imaging provides a novel approach for intelligent geological perception in tunnelling and underground engineering due to its high spectral resolution, nondestructive nature, and combined spectral-spatial information. However, in confined underground spaces, noise is often introduced by short exposure times, low illumination, and dust, and limited spatial resolution can cause mixed pixel effects, complicating data processing. This study presents an underground hyperspectral imaging-based mineral mapping method that achieves wall-rock visualization and semi-quantitative mineral mapping through image denoising and spectral unmixing. A spatial-spectral recurrent transformer U-Net is developed to reduce noise by leveraging spectral band correlations and nonlocal spatial-texture dependencies. A Dirichlet-based mixed pixel simulation is used to address spectral mixing, with the N-FINDR algorithm identifying endmember minerals, and the fully constrained least squares method to estimate mineral abundances. When applied to a water diversion tunnel in Shanxi, the method generates spatial distribution maps of dolomite and calcite. The experimental results confirm its effectiveness for intelligent geological logging and subsurface geological feature analysis.

Key words

Hyperspectral imaging / Underground space / Geological analysis / Mineral mapping / Hyperspectral noise reduction / Hyperspectral unmixing

Cite this Article

Shan Li, Peng Lin, Kai Yang, Zhenhao Xu. In situ, non-destructive and rapid mineral mapping in tunnels with hyperspectral imaging[J]. Underground Space, 2026 , 27 : 301 -320 . DOI: 10.1016/j.undsp.2025.11.003

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Funding

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National Natural Science Foundation of China(52379103; 52279103)
Natural Science Foundation of Shandong Province(ZR2023YQ049)

Appendix

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Year 2026 volume 27 Issue 0

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Article Info

doi: 10.1016/j.undsp.2025.11.003

Receive Date：2025-06-16
Online Date：2026-06-17
Published：2026-04-10

Article Data

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History

Received：2025-06-16
Revised：2025-11-03
Accepted：2025-11-08

Funding

National Natural Science Foundation of China(52379103; 52279103)

Natural Science Foundation of Shandong Province(ZR2023YQ049)

Affiliations

^aState Key Laboratory for Tunnel Engineering, Institute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, China

^bSchool of Qilu Transportation, Shandong University, Jinan 250061, China

Corresponding:

^* State Key Laboratory for Tunnel Engineering, Institute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, China. E-mail address: zhenhao_xu@sdu.edu.cn (Z. Xu).

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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