Ground Penetrating Radar (GPR) is a non-destructive geophysical tool that emits electromagnetic waves and captures their echoes to image subsurface structures. However, criticism of conventional imaging persists stems from quantitative ambiguity or limited practicability as deterministic or probabilistic inversion typically suffers from high computational costs, accuracy variability, and initial model sensitivity. To address these issues, this paper develops a deep learning inversion alternative based on the RCUNet-Transformer network. Specifically, we exploit a lightweight variant of the RFDB-UNet version as the backbone, with a special transformer encoder module designed to underscore global context feature extraction, and a convolutional block attention module (CBAM) leveraged to prioritize the crucial shallow zones while minimizing attention to irrelevant ones. The network performance is evaluated extensively through the use of synthetic records with diverse conditions, laboratory experiments, and on-site complex field datasets. Our evaluation of reconstruction quality involves utilizing three auxiliary metrics to assess global consistency, scale-invariant features, and edge-level detail preservation, as well as two basic indicators to assess pixel-wise fidelity and structural similarity. The results demonstrate the outperformance of the proposed method over other networks, especially for field dataset elaboration, which showcases its generalization capability in terms of practicability and quantitative accuracy for the potential of practical engineering applications.

Ground penetrating radar (GPR) inversion is a well-accepted technique for tunnel geological prediction by reconstructing the permittivity distribution in front of the tunnel face. However, full waveform inversion (FWI) still retains practical obstacle mainly due to the limited illumination in the common-offset gathering mode. Moreover, the narrow width of tunnel face, initial model sensitivity, together with unaffordable computational cost could exacerbate the ill-posedness problem of the FWI for real-time prediction. The conventional U-Net network typically uses skip connections to directly fuse shallow and deep features, without differentiating the weight of each feature channel, and lacks an effective constraint mechanism. Consequently, the featural extraction capability of U-Net is rather limited, especially for the EM response details concerning small-scale geological anomalies. To mitigate this, we propose a novel TGPInvNet framework for real-time GPR inversion in tunnel geological prediction. The network is an improved U-Net for adaptive focus on small-scale features by incorporating the attention gate (AG) mechanism, convolution block attention module (CBAM). More importantly, the multi-task learning (MTL) module is introduced to provide constraints and information for the inversion. Both synthetic and field experiments are conducted to assess the performance of the proposed network against state-of-the-art methods, include GPRNet, PINet, and TV-regularized FWI. For field validation, four quantitative metrics (i.e., MSE, MAE, SSIM, and MSSIM with values of 0.0047, 0.0495, 0.7467, and 0.7469, respectively) demonstrate the potential application and visible practicality of the proposed TGPInvNet.

The essentiality of diffraction wavefront in structural mapping has been perceived, mainly because of its imaging potential for small structural variations and microscopic discontinuities. However, full extraction of ground penetrating radar (GPR) diffractions cannot be done perfectly even in theory as the faint components are typically masked by the more dominant reflections, resulting in a resolution loss of minutiae features. To address these issues, this letter develops a semblance-based coherence analysis framework for GPR diffraction wavefront reconstruction and full waveform inversion (FWI). Specifically, adaptive amplitude restoration is initially performed using the mean instant amplitude for analytic attenuation function derivation, which is followed by modified multitrace coherence stacking. Diffraction reconstruction is then executed with corrective factors determined by EM semblance attributes, while being assessed by quantified indices for reconstruction performance. The proposed framework is tested for validity and practicality with a synthetic model and laboratory experiment by comparing the conventional FWI and the diffraction driven FWI. The results present a high-resolution image of diffraction occurrences even with a homogeneous initial model, and more intuitively show a higher-resolution image even with a suboptimal initial model by diffraction-driven permittivity analysis. These encouraging results could potentially enable a hybrid inversion which benefits from reconstructing both GPR reflections and diffractions.

Ground-penetrating radar (GPR) has been regarded as a potentially efficient way of evaluating the growth status of trees and preventing deterioration associated with trunk defects. The majority of current GPR data inversions, however, focused on imaging the macroscale location of defects. As the first attempt to seek a preferable quantitative inversion methodology for specifying tree protection and remedies, this article proposes a full-waveform inversion (FWI) approach involving dual-parameter attributes applied to common-offset GPR data from a commercial antenna. Specifically, the synchronous inversion of both dielectric constant and conductivity improves the identification accuracy of certain defect types. In particular, both a multifrequency strategy and total-variation (TV) regularization are seamlessly introduced to assure inversion stability by overcoming local minima and cycle skipping. Through an irregular trunk model test, the effectiveness of the optimized inversion is initially verified by presenting the precise features of the crack, hollow, and decay with the dual-parameter inversion results. In addition, several other synthetic trunk models and in-site trunk model tests further demonstrate the robustness and practicability of the proposed algorithm, which can offer more specific and comprehensive guidance for the formulation of tree protection and restoration measures.

Diffraction separation is considered of great importance for the full-wavefront reconstruction (FWR) of ground penetrating radar (GPR) and its delicate data processing. In contrast to reflections, GPR diffraction events encode the fully dynamic feature and subwavelength information to identify small-scale heterogeneities, thereby offering enhanced illumination and resolution for GPR imaging. However, fully extracting these faint diffraction components remains a very challenging task. These components are usually ignored because of the strong interference and masking effect of the more dominant reflective components, and in particular, the attenuation of GPR waves accelerates almost exponentially with the frequency. In this article, we developed a coherence analysis (CA) framework for extracting GPR diffractions by considering the kinematic attributes and discontinuity features of GPR wavefronts, as well as the dispersion and attenuation effects of GPR responses. To be specific, the compensation preprocessing and semblance summation are performed sequentially for coherence acquisition, after which a multiscale subtraction is designed toward the coherent fields. In particular, we concentrated on analyzing media losses and multiparameter impacts on diffraction separation performance. To demonstrate the feasibility and practicality of the proposed framework, we tested our algorithm using synthetic and field datasets, both results with quantified metrics showing the elimination of continuities. The comparative results reveal the performance of the framework in preserving the edge components of GPR diffractions, making it potentially attractive for microscopic velocity analysis, refined full wavefield migration (FWM), and well-constrained full waveform inversion in terms of small-scale heterogeneities and discontinuities.

Subsurface mapping can be implemented by the migration imaging of ground penetrating radar (GPR) data. Finite difference (FD)-based migration can effectively achieve fast imaging in media with lateral heterogeneity. However, this migration inherently suffers from insufficient accuracy in determining the true morphology of any steep-obliquity interface. In this letter, we propose a new algorithm based on crossed differential operator (CDO) and reverse-time migration (RTM) strategy to refocus the reflections back into its precise location. By employing the implicit Crank–Nicolson scheme without any operator approximations, wave field can be extrapolated nearly along the characteristic directions of wave propagation based on the nonapproximate equations. We determined the potential of the algorithm using synthetic and field data. For synthetic data, we consider a multigeometry scenario, including different angle interfaces, different-sized spheres, and lateral heterogeneity. In particular, we focus on extrapolation accuracy, amplitude–frequency focusing ability, entropy analysis, and imaging errors. In the field data test, we found that the finer morphology of the steeper-obliquity interfaces can be determined, the interpretability and amount of discernible details can also be improved. This improvement makes CDO-based migration a very promising tool for the finer imaging of shallow subsurface.

Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is becoming a promising technique to facilitate the interpretation of surface-GPR data and the mapping of the subsurface. However, more general FWIs still require a sufficient amount of RAM memory, and it is difficult to produce an accurate and representative reconstruction result due to a large amount of the Hessian matrix calculations and singular value decomposition (SVD). In this article, we developed a novel full-waveform approach of the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm for surface-GPR data that is based on a quasi-Newton framework and the total variation (TV) regularization. The proposed approach uses an L-BFGS algorithm and combines a scale-transformation regularization technique to mitigate the ill-posed problem of inversion, which can impose a biparameter preinformation constraint to ensure the stability of inversion, and adaptive regularization weights are applied to improve the convergence efficiency of inversion. To demonstrate the novelty and effectiveness of the proposed scheme, we tested our FWI algorithm using synthetic data and in-site field data. In the testing, we focus on analyzing the influence of different aspects of the FWI results, including different scale factors, regularization weights, inversion strategies, acquisition configurations, initial models, and the noisy data set. In particular, the FWI experiment is performed to demonstrate the applicability of the proposed algorithm. The results show that the proposed algorithm can effectively reconstruct the biparameter near the subsurface with high accuracy, which makes our approach very attractive for attribute analysis applications and makes the surface-GPR FWI commercially viable.

Efficient and accurate simulation of ground penetrating radar (GPR) in the open region helps immensely in both grasping the features of echoes and facilitating the interpretation of real GPR data. Due to the limitation of the computer model, however, the strong artificial boundary reflections, especially the low-frequency propagating waves encountered at the late stage of simulation greatly affect the simulation accuracy of GPR. This paper presents an innovative optimized unsplit-field convolutional perfectly matched layer (UCPML) based on rotated staggered grid (RSG) scheme to truncate the finite-difference time-domain (FDTD) lattices. Rather than obey the sharp variation based on an m th-order polynomial, the optimized approach employs a novel optimized term and an adjustment factor to seek a gentle variation on optimal constitutive coefficients. This guarantees that the determination of optimal constitutive coefficients can be less influenced by the order of polynomial and especially, to improve the absorptive performance on low-frequency propagating waves. The calculating efficiency and accuracy of the RSG-FDTD scheme, as well as the absorbing performance of the optimized UCPML, are verified by two numerical examples. In particular, the analysis of the amplitude-frequency features of low-frequency clutters at steady state of the electromagnetic (EM) field and the corresponding global reflection error in the time-frequency domain is also presented.