深埋长大隧道热红外像素级地温预测方法

doi:10.3724/1000-6915.jrme.2024.0850

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Abstract To enhance the spatial accuracy of underground temperature (UGT) prediction for deeply buried long tunnels (DBLT) in complex geomorphic environments, a pixel-level UGT prediction methodology known as the Virtual Temperature Column Method is proposed, which utilizes thermal infrared (TIR) remote sensing data. This innovative approach incorporates multi-year surface radiation data collected from TIR satellites as boundary conditions for surface temperature, leveraging a limited dataset of temperature measurements from boreholes. Key parameters such as the annual average surface temperature (AAST)，the heat flux value in the thermostat layer (THF), and the thermal conductivity of the rock (RTC) are obtained through surface temperature inversion, theoretical calculations, and indoor experiments, respectively. These parameters facilitate high-resolution pixel-level predictions of UGT in DBLT. To validate the efficacy of this methodology, a project in the Qinghai—Tibet Plateau region is used as an example to forecast the UGT of deep tunnel strata. Additionally, a gray correlation analysis is conducted to identify the key factors influencing the sensitivity of UGT predictions. The results demonstrate that the methodology improves the spatial accuracy of UGT predictions from a kilometer scale to a 30-meter scale, with prediction errors between the predicted and measured values controlled within 6%, indicating high spatial accuracy. The three key parameters affecting the sensitivity of the UGT prediction are AAST, THF, and RTC, with their correlation coefficients being 1, 0.99, and 0.489, respectively, indicating the hierarchy of sensitivity as follows: AAST＞THF＞RTC. This methodology effectively addresses the challenges posed by scarce borehole temperature measurement data and the low spatial accuracy of UGT predictions in complex geomorphic environments, providing a valuable reference for accurate UGT prediction in deep-buried tunneling projects in regions of frequent geothermal activity in China.

Key words： tunnelling engineering underground temperature prediction thermal infrared remote sensing land surface temperature retrieval volumetric accuracy thermostat layer

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	Articles by authors
	LIU Zhongbo1
	2
	ZHU Yong1
	2
	ZHOU Hui1
	2
	ZHANG Chuanqing1
	2
	WANG Dong3

Cite this article:

LIU Zhongbo1,2,ZHU Yong1, et al. Thermal infrared pixel-level underground temperature prediction method for deeply buried long tunnels[J]. , 2025, 44(S2): 215-227.

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https://rockmech.whrsm.ac.cn/EN/10.3724/1000-6915.jrme.2024.0850 OR https://rockmech.whrsm.ac.cn/EN/Y2025/V44/IS2/215

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