融合图像-文本多模态探测信息的围岩级别智能预报

doi:10.3799/dqkx.2026.146

融合图像-文本多模态探测信息的围岩级别智能预报

doi: 10.3799/dqkx.2026.146

甘超^1,2,3,
张萌^1,2,3,
曹卫华^1,2,3, ,

1. 中国地质大学(武汉）人工智能与自动化学院, 湖北武汉 430074;
2. 复杂系统先进控制与智能自动化湖北省重点实验室, 湖北武汉 430074;
3. 地球探测智能化技术教育部工程研究中心, 湖北武汉 430074

基金项目:

国家自然科学基金重点项目(No.62333019)。

详细信息

作者简介:
甘超(1990-),男,副教授,主要从事复杂系统建模与优化控制的研究工作.E-mail:ganchao@cug.edu.cn

通讯作者:
曹卫华(1972-),E-mail:weihuacao@cug.edu.cn

中图分类号: U452.12
计量
- 文章访问数: 62
- HTML全文浏览量: 0
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2026-02-01
- 网络出版日期: 2026-06-29

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

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

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

摘要

摘要: 深部地质工程地质条件复杂、隐蔽性强，施工过程存在较多安全隐患，准确预报围岩级别对保障高效施工至关重要。本文针对破碎岩体特征的不易界定性提出了融合图像-文本多模态探测信息的围岩级别智能预报方法。使用依据里程范围对齐的综合物探图像，由图文跨模态匹配模型训练得到物探图像响应特征识别模型，用于提取破碎岩体响应特征；由目标检测模型对破碎岩体响应特征进行定位；进而融合物探图像的文本描述特征、破碎岩体目标检测框序列特征及相关地质参数，构建多维统一特征向量，利用多输出随机森林模型实现探测里程范围内围岩级别的准确预报。通过文本预报结论和目标框预报结论的相互验证，有效避免了不易界定的破碎岩体特征的误报和漏报，实现了由多源物探图像到围岩级别等级的预报，在实际测试中准确率达到92%，为深部地质工程超前智能预报工作奠定了重要基础。
- 深部地质工程 /
- 围岩级别预报 /
- 多模态物探信息 /
- 图像识别
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

HTML全文

参考文献(33)

[1]	Zou Y., Dong X. J., Feng T., et al., 2025. Research on spatial prediction technology for mitigating tunnel inrush disasters under complex geological conditions in China's Hengduan Mountain Range,Scientific Reports, 15(1): 1850-1850.
[2]	Sun H., Sheng L., Dai Y., et al., 2025. 3D geological modeling of tunnel alignment in the complex mountainous region of Yongshan, China, based on multisource data fusion,Engineering Geology, 354: 108209-108209.
[3]	Sabri S. M., Jaiswal A., Verma K. A., et al., 2025. Systematic Review of RMR, Q-System, and GSI in Tunnel Classification: Origin, Advancement, and Limitations,Indian Geotechnical Journal, (prepublish): 1-23.
[4]	Li X., Zhang X., Liu Q., et al., 2025. Evaluation of rock mass quality and its mechanical properties through digital drilling process monitoring,Journal of Rock Mechanics and Geotechnical Engineering, 17(7): 4490-4511.
[5]	Zhang Y., Zhou J., Li J., et al., 2025. Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis,Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 11(1): 53-53.
[6]	Gan C., Wang Y., Cao W. H., et al., 2025. Real-time formation drillability sensing-based hybrid online prediction method for the rate of penetration (ROP) and its industrial application for drilling processes,Control Engineering Practice, 164: 106487-106487.
[7]	Zhao Z. P., Chen J. X., Geng Q., et al., 2024. Construction Technology and Application of TBM in Shengli Tunnel of Tianshan Mountain under poor geological conditions,Stavební Obzor Civil Engineering Journal, 33(2): 168-180.
[8]	Liu C., Li J., Liu Z., et al., 2025. A comprehensive review of data processing and target recognition methods for ground penetrating radar underground pipeline B-scan data,Discover Applied Sciences, 7(4): 310-310.
[9]	Zheng P., Zhang A., Shi Z., et al., 2025. TLAD-YOLO: Lightweight network for intelligent detection of railway tunnel lining anomalies using ground penetrating radar,Journal of Applied Geophysics, 241: 105869-105869.
[10]	Dong Z., Zhang X., Yang W., et al., 2025. Using a Hybrid of the U-Net and Multilayer Perceptron Models to Automatically Predict the Geological Strength Index from Tunnel Face Rock Joint Images,Rock Mechanics and Rock Engineering, 58(11): 1-18.
[11]	Yu X. Y., Dong N., Zhu L. H., et al., 2025. CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification,IEEE Transactions on Multimedia, 27: 4137-4150.
[12]	An J., Su P., Liu J., et al., 2025. Implementation of YOLO-CLIP fusion algorithm for fall detection,Signal, Image and Video Processing, 19(10): 837-837.
[13]	Saeed F., Aldera S., Shamma’a A. A. A., et al., 2024. Rapid adaptation in photovoltaic defect detection: Integrating CLIP with YOLOv8n for efficient learning,Energy Reports, 12: 5383-5395.
[14]	Li J. F., Sun S. Y., Zhang K., et al., 2025. Single-stage zero-shot object detection network based on CLIP And pseudo-labeling,International Journal of Machine Learning and Cybernetics, 16(2): 1055-1070.
[15]	Xu Z., Wang Z., Li S., et al., 2024. GeoPredict-LLM: Intelligent tunnel advanced geological prediction by reprogramming large language models,Intelligent Geoengineering, 1(1): 49-57.
[16]	Fang Z. H., Tan Z. H., Zhu Z., et al., 2023. Detection of highway disease images using ground-penetrating radar based on Faster R-CNN.Construction Technology (Chinese and English), 52(24): 76-82. (in Chinese)
[17]	Qin Z. X., Jiang Y. N., Xu L., et al., 2023. Automatic anomaly detection of ground-penetrating radar road images based on YOLO algorithm.Science Technology and Engineering, 23(27): 11505-11512. (in Chinese)
[18]	Wang W. W., Mou Y. C., 2021. Application of TSP tunnel advanced geological forecasting technology in large faults.Journal of Engineering Geophysics, 18(2): 186-193. (in Chinese)
[19]	Zhang X. J., 2023. Application of transient electromagnetic method in advanced geological forecasting of water diversion tunnels.Heilongjiang Traffic Science and Technology, 46(10): 102-105. (in Chinese)
[20]	Zhang S., Zhen D. Y., Liu K., et al., 2024. Application of 3D visualization of TSP and transient electromagnetic results in tunnel fault water-bearing forecasting.China Railway, (5): 75-81. (in Chinese)
[21]	Lin Z., Feng S., Fan G. Y., et al., 2021. Application of geological radar in advanced geological forecasting of a tunnel.Exploration Science and Technology, (3): 47-51+64. (in Chinese)
[22]	Gao S. Q., Wang Y. Q., Mou Y. C., 2021. Advanced geological forecasting and image analysis of karst tunnels using geological radar.Journal of Engineering Geophysics, 18(5): 642-646. (in Chinese)
[23]	Long G. Y., Zheng L., 2024. Research on surrounding rock classification based on tunnel advanced geological detection.Science and Technology Innovation and Application, 14(6): 124-127. (in Chinese)
[24]	Jiang Y., Wang H. L., Chen Z., 2024. Intelligent prediction algorithm for advanced forecasting images of adverse tunnel geological bodies based on deep learning.Modern Tunnel Technology, 61(3): 148-156. (in Chinese)
[25]	房振华, 谭治海, 朱哲, 等, 2023. 基于Faster-RCNN的探地雷达公路病害图像检测, 施工技术(中英文), 52(24): 76-82.
[26]	覃紫馨, 姜彦南, 徐立, 等, 2023. 基于YOLO算法的探地雷达道路图像异常自动检测, 科学技术与工程, 23(27): 11505-11512.
[27]	王汪汪, 牟元存, 2021. TSP隧道超前地质预报技术在宽大断层内部的应用, 工程地球物理学报, 18(2): 186-193.
[28]	张旭杰, 2023. 瞬变电磁法在引水隧洞超前地质预报中的应用, 黑龙江交通科技, 46(10): 102-105.
[29]	张硕, 甄大勇, 刘康, 等, 2024. TSP与瞬变电磁成果三维化在隧道断层含水预报中的应用, 中国铁路, (5): 75-81.
[30]	林志, 冯森, 范国宇, 等, 2021. 地质雷达在某隧道超前地质预报中的应用, 勘察科学技术, (3): 47-51+64.
[31]	高树全, 王玉琴, 牟元存, 2021. 岩溶隧道地质雷达超前地质预报及图像分析, 工程地球物理学报, 18(5): 642-646.
[32]	龙贵云, 郑亮, 2024. 基于隧道超前地质探测的围岩分级研究, 科技创新与应用, 14(6): 124-127.
[33]	蒋源, 王海林, 陈兆, 2024. 基于深度学习的隧道不良地质体超前预报图像智能预测算法, 现代隧道技术, 61(3): 148-156.