中外公路2026，Vol.46Issue(4)：243-251,9.DOI:10.14048/j.issn.1671-2579.2026.04.026
复杂不良地质隧道超前GPR图像智能反演方法
Intelligent inversion of advanced GPR images for complex adverse geological tunnels
摘要
Abstract
To address the limitations of traditional empirical interpretation methods for advanced ground penetrating radar(GPR)detection images of tunnels,including inconsistent standards,low efficiency,and high rates of misinterpretation and missed detection,this study proposed an intelligent inversion method for radar B-scan waveform images of adverse geology based on an improved generative adversarial network(GAN).A multi-condition synthetic dataset was constructed through electromagnetic numerical simulation combined with random generation constraints of medium parameters,incorporating three types of geological anomalies(irregular cavities,fracture zones,and cracks)and their combinations.The GAN generator was optimized by integrating Unet convolutional neural network architecture and dilated convolution modules,and structural similarity(SSIM)loss function was incorporated into the loss function to enhance the extraction capability of waveform image features and model inversion performance.Training evaluation results demonstrate that the improved GAN model achieves approximately 4%and 10%higher inversion accuracy than conventional GAN and standalone Unet models,respectively.An inversion experiment using actual engineering data from fracture zone prediction was carried out.Comparative analysis with digital drilling and excavation verification data confirms that the inversion prediction results of the proposed method are basically consistent with actual conditions,and it can provide reliable geological forecasting for tunnel excavation and support.关键词
隧道工程/超前预报/地质雷达/深度学习/图像反演Key words
tunnel engineering/advanced prediction/ground penetrating radar/deep learning/image inversion分类
交通工程引用本文复制引用
唐文斌,梁铭,彭浩..复杂不良地质隧道超前GPR图像智能反演方法[J].中外公路,2026,46(4):243-251,9.基金项目
广西重点研发计划项目(编号:桂科AB22080033) （编号:桂科AB22080033）
