基于地质特征引导多源探测信息融合的岩体完整性预报方法

吴炼; 曹卫华; 甘超

doi:10.3799/dqkx.2026.125

Volume 51 Issue 8

Aug. 2026

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Article Navigation > Earth Science > 2026 > 51(8): 3158-3169

Wu Lian, Cao Weihua, Gan Chao, 2026. Geological Feature-Guided Fusion of Multi-Source Probing Information for Rock-Mass Integrity Prediction. Earth Science, 51(8): 3158-3169. doi: 10.3799/dqkx.2026.125

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Wu Lian, Cao Weihua, Gan Chao, 2026. Geological Feature-Guided Fusion of Multi-Source Probing Information for Rock-Mass Integrity Prediction. Earth Science, 51(8): 3158-3169. doi: 10.3799/dqkx.2026.125

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Geological Feature-Guided Fusion of Multi-Source Probing Information for Rock-Mass Integrity Prediction

doi: 10.3799/dqkx.2026.125

Wu Lian^{1, 2, 3
,
,},
Cao Weihua^{1, 2, 3
,
,
,},
Gan Chao^{1, 2, 3}

1.
School of Artificial Intelligence and Automation, China University of Geosciences (Wuhan), Wuhan 430074, China
2.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3.
Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China

Received Date: 2026-01-30
Publish Date: 2026-08-25

Abstract

Abstract

In deep geological engineering, accurate prediction of rock-mass integrity and sensing of geological conditions ahead are important for construction safety and efficiency. However, conclusions from multiple probing methods often conflict due to differences in sensing mechanisms, noise interference, and other factors, making effective fusion difficult. This paper proposes a geology-guided fusion method for rock-mass integrity prediction using multi-source probing information. Geological features serve as guidance, and a cross-attention mechanism adaptively adjusts the contribution of each probing conclusion under actual geological conditions, thereby resolving conflicts among multi-source results and generating a fused integrity prediction. Applied to a real deep tunnel project, the proposed method outperforms several commonly used methods in prediction accuracy and yields highly interpretable results.
- rock mass integrity prediction,
- geological environment perception,
- multisource information fusion,
- cross-attention,
- data fusion

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References(32)

References

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