融合图像-文本多模态探测信息的围岩级别智能预报

甘超; 张萌; 曹卫华

doi:10.3799/dqkx.2026.146

Volume 51 Issue 8

Aug. 2026

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Gan Chao, Zhang Meng, Cao Weihua, 2026. Intelligent Prediction of Surrounding Rock Classification by Fusing Image-Text Multimodal Detection Information. Earth Science, 51(8): 3145-3157. doi: 10.3799/dqkx.2026.146

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Gan Chao, Zhang Meng, Cao Weihua, 2026. Intelligent Prediction of Surrounding Rock Classification by Fusing Image-Text Multimodal Detection Information. Earth Science, 51(8): 3145-3157. doi: 10.3799/dqkx.2026.146

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Intelligent Prediction of Surrounding Rock Classification by Fusing Image-Text Multimodal Detection Information

doi: 10.3799/dqkx.2026.146

Gan Chao^{1, 2, 3
,
,},
Zhang Meng^{1, 2, 3},
Cao Weihua^{1, 2, 3
,
,
,}

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

Received Date: 2026-01-23
Publish Date: 2026-08-25

Abstract

Abstract

Deep geotechnical engineering involves complex, hidden geology and high risks, making accurate rock classification critical. This study presents an intelligent prediction method that fuses image-text multimodal geophysical data via mileage-aligned survey images. A cross-modal matching model extracts fractured-rock features, localized by an object detector. Text descriptors, bounding-box sequences, and geological parameters are combined into a unified vector; a multi-output random forest predicts rock classes along mileage. Mutual correction between text and detection reduces false alarms and missed detections. Field tests show 92% accuracy, supporting intelligent advance forecasting.
- deep geological engineering,
- surrounding rock classification,
- multimodal geophysical information,
- image recognition

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References(33)

References

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Intelligent Prediction of Surrounding Rock Classification by Fusing Image-Text Multimodal Detection Information

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