陕西煤炭2026，Vol.45Issue(4)：158-162,5.DOI:10.20120/j.cnki.issn.1671-749x.2026.0428
赵庄二号井随掘监测及超前灾害预测方法
Monitoring-while-excavation and advanced hazard prediction method for Zhaozhuang No.2 well
刘剑奇 1高林 2窦文武 2余俊辉 3关奇3
作者信息
- 1. 山西晋煤集团赵庄煤业有限责任公司赵庄二号井,山西长治 046605
- 2. 山西晋煤集团技术研究院有限责任公司,山西晋城 048006||山西省矿井地球物理勘探技术创新中心,山西晋城 048006
- 3. 中煤科工西安研究院(集团)有限公司,陕西西安 710077
- 折叠
摘要
Abstract
With the rapid advancement of intelligent coal mining technologies,seismic monitoring while excavation has grad-ually become one of the core technologies for enhancing excavation safety and work efficiency.In traditional coal mine exca-vation processes,advance detection typically requires halting the excavation at the working face.This not only limits the real-time nature of detection but also causes work stoppages,severely impacting overall tunneling progress and mine safety man-agement.We propose an innovative target detection method based on diffraction imaging results from seismic monitoring while excavation,combined with deep learning techniques to improve advanced seismic detection during excavation.By pro-cessing and imaging real-time monitoring data,this study integrates Generative Adversarial Networks(GAN)with the Faster R-CNN object detection model,leveraging the powerful features of deep learning to significantly enhance the accuracy and robustness of target detection.The research results indicate that the proposed method can efficiently and accurately identify potential geological anomaly zones within the coal mine working face,such as faults and other complex geological structures.This provides more real-time and precise safety warnings for mine excavation and enables the early detection of potential hazards while ensuring normal excavation at the working face.Practical application and validation in the Zhaozhuang No.2 Mine project fully demonstrate the significant advantages of this method in improving prediction accuracy and detection effi-ciency.It offers strong technical support for support and hazard prevention in mine tunneling faces,promotes the develop-ment of intelligent coal mine monitoring systems,and provides new solutions and technical safeguards for future mine safety management.关键词
随掘地震探测/Faster R-CNN-GAN/矿山智能化/目标检测/绕射法成像Key words
seismic monitoring during excavation/Faster R-CNN-GAN/intelligent mine/target detection,diffraction imaging分类
天文与地球科学引用本文复制引用
刘剑奇,高林,窦文武,余俊辉,关奇..赵庄二号井随掘监测及超前灾害预测方法[J].陕西煤炭,2026,45(4):158-162,5.
