基于自适应衰减补偿模型的探地雷达偏移成像及应用研究

Migration Imaging of Ground Penetrating Radar Using an Adaptive Attenuation Compensation Model and Its Applications

为提升隧道复杂围岩条件下雷达法的成像分辨率及检测精度,提出一种基于瞬时属性分析的探地雷达数据自适应衰减补偿模型及逆时偏移技术。通过迭代计算剖面数据中样本点的瞬时幅值,提取时间步的瞬时振幅谱特性及幅值属性,并将瞬时值随时间变化的特征函数作为信号衰减的规律表征,建立自适应指数-线性多项式拟合衰减特征,重构可解析的雷达信号衰减补偿模型;利用衰减补偿模型的逆时偏移成像,最大限度保留有耗媒质深部的异常特征细节信息,实现病害体的高保真成像。以仰拱支护结构的数值模型算例和隧底衬砌实测数据测试该方法的有效性和实用性。结果表明:1)瞬时幅值属性极值主要集中在绕射叠加区及多次反射区;2)多项指数自适应补偿能客观表征雷达数据的衰减特征,确保剖面的高保真特性;3)高信噪比数据可采用n≥5阶次补偿,低信噪比数据宜采用n≥9阶次补偿。

To improve the imaging resolution and detection accuracy of ground penetrating radar(GPR)methods in complex surrounding rock conditions in tunnels,a reverse time migration approach is proposed combined with an adaptive attenuation compensation model.This approach involves deriving the instantaneous amplitude for each sample from the collected data profile by extracting the instantaneous amplitude spectrum and its attributes within each time window.The method uses these time-dependent instantaneous amplitude attributes,which reflect the signal's attenuation along the profile to construct an adaptive linear exponential polynomial that matches the observed attenuation characteristics.This results in an analytical signal attenuation model that is used for compensation.By applying this attenuation-compensated model to reverse time migration imaging,it is aimed to maximize the retention of deep anomalous detail in lossy media,thus improving the potential for high-fidelity imaging of structural defects.The feasibility and practicality of the method are evaluated using a synthetic invert arch model and field experiments conducted on tunnel bottoms and linings.The results indicate:(1)The diffraction superposition and multiple reflection regions exhibit the highest extremes in instantaneous amplitude attributes.(2)Multiple exponent adaptive compensation can objectively characterize radar signal attenuation and ensure high-fidelity profile characteristics.(3)Data with a high signal-noise ratio(SNR)can be compensated by using n≥5 order,while data with a low SNR should be compensated by using n≥9 order.