Yi Lei

Ground-penetrating radar (GPR) diffractions typically manifest weak responses resulting from electromagnetic wave interactions with subwavelength heterogeneities (e.g., cracks and voids) or geometric discontinuities (e.g., scattering edges and corners). These subtle yet informative wavefield components are critical for enabling enhanced illumination and dedicated imaging of subsurface microfeatures in infrastructure assessment and geological hazard mitigation. Current diffraction extraction methods in GPR face significant practical limitations: kinematic local-slope techniques are computationally intensive and may lead to loss of vertex events, whereas coherence analysis (CA) requires meticulous calibration of scaling factors and time-moveout corrections. Such constraints can compromise the resolution of imaging modalities, such as migration, tomography, and full-waveform inversion (FWI). To address these challenges, this study introduces an adaptive diffraction extraction workflow that integrates three core modules in wavelet and dip domains: a wavelet-based reflection model for structural preservation, dip-adaptive smoothing operators for wavefield decomposition, and particle swarm optimization (PSO) with multiobjective criteria (Laplacian sharpness, wavelet entropy (WE), and focusing metrics) for parameters optimization. The workflow iteratively extracts residual diffractions by optimizing wavelet decomposition levels, smoothing scales, and residual thresholds using PSO-driven fitness functions. Validated through the heterogeneous random media model, the Sigsbee2a benchmark, and field experiments, the PSO-guided workflow demonstrates quantifiable improvements in edge preservation, coherence artifacts reduction, spatial resolution, and kinematic consistency. By prioritizing the wavefield integrity and structural fineness, the proposed method provides refined inputs for subsequent imaging, highlighting its potential to advance the resolution and reliability of near-surface GPR sensing applications.

Reverse-time migration (RTM) is widely used for ground penetrating radar (GPR) data processing, typically formulated assuming nondispersive wave propagation with frequency-independent features. However, real subsurface media exhibit dispersion and attenuation, causing GPR waves to suffer significant amplitude, phase, and travel time alterations compared to nondispersive cases. Conventional RTM neglects frequency-dependent attenuation and velocity dispersion, degrading imaging accuracy and resolution. To provide a feasible solution for achieving high-precision migration imaging of GPR data from high-dispersive and attenuating geological structures, as well as from weak deep GPR signals, we address this limitation by proposing a novel GPR RTM algorithm incorporating joint compensation for relaxation and Ohmic losses, based on the time-reversal invariance principle. This method employs finite element forward modeling and reverse-time extrapolation on triangular meshes, achieving loss compensation by reversing loss terms in the time-reversed equation to restore temporal symmetry. A zero-time imaging condition is adopted, and a time-varying Butterworth filter suppresses amplified noise for stable extrapolation. Validated through three representative Debye-type dispersive synthetic datasets and in-situ experimental GPR data from wet sand detection, our proposed approach mitigates energy attenuation when compared to conventional RTM and individual loss compensation methods. The results demonstrate superior imaging resolution and enhanced reflectivity amplitudes, while accurately recovering energy losses caused by both Ohmic and relaxation mechanisms.

Ground penetrating radar (GPR) is a nondestructive geophysical tool that emits electromagnetic (EM) waves and captures their echoes to image subsurface structures. However, criticism of conventional imaging persists, stemming 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 article develops a deep learning (DL) inversion alternative based on the RCUNet-Transformer network. Specifically, we exploit a lightweight variant of the RFDB-U-Net 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.

Accurately characterization of pipeline leakage by capturing time-lapse features and their pathway orientations using ground penetrating radar (GPR) is crucial for optimizing the operational efficiency of water supply systems and mitigating water resource losses. However, leakage imaging concerning temporal-spatial details remains a significant challenge for current deterministic or probabilistic inversions due to the complexity of signals, environmental interference, and computational burden involved in inversion. This paper develops a novel attention-guided GPR inversion framework, LeakInv-CUNet, to implement refined leakage imaging of time-lapse features and plume pathways. To enhance the training of the LeakInv-CUNet network, extensive GPR datasets are created by augmenting data with simulations and experimental detections to account for variations in injection orientation, plume evolution, and the properties of the subsurface media. By leveraging the dual virtues of Convolutional Block Attention Module (CBAM) and UNet architecture, the developed LeakInv-CUNet framework is capable of extracting critical and even slight response features triggered by leakage anomalies, allowing for refined imaging of leakage plumes and their orientations. Specifically, the training is fed by the inversion of GPR datasets corresponding to different permittivity distributions of leakage, which is subsequently feedbacked through functional mapping of the Topp equation nexus to water content distribution imaging. In particular, we concentrate on the leakage features by taking into account the effects of water injection orientation and time-lapse characteristics. Simulation and on-site field experiments are conducted to assess its performance in terms of anti-noise robustness and practicality compared to the conventional UNet and Enc-Dec networks. With a mean absolute percentage error (MAPE) of attention-guided leakage imaging less than 0.707%, and a structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) greater than 0.924 and 45.416, respectively, the attention-guided leakage imaging exhibits excellent efficiency and accuracy for potential applications in early leakage warning and pipeline time-lapse monitoring.

Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs.

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 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 multi-trace 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.

As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability to suppress artifacts and generate high-resolution images in comparison to conventional RTM. However, in the particular case of GPR detection methods, LSRTM is extremely susceptible to aliasing artifacts caused by under-sampling. In pursuit of enhanced precision in underground structure characterization, this paper presents the development of a new LSRTM based on modified total variation (MTV) regularization to improve imaging resolution. Initially, the objective function of LSRTM is derived by combining the Born approximation in 2-D transversal magnetic mode. Next, the adjoint equations and their gradients are solved using the Lagrange multiplier method. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. In the numerical experiments, in comparison to the conventional LSRTM method, the LSRTM-MTV algorithm demonstrated a 30.4% increase in computational speed and a 21.1% reduction in mean squared error (MSE). The outperformance of the proposed method is verified in detail through the image resolution and amplitude preservation in the test of synthetic data and laboratory data. Future research efforts will center on applying the proposed method to models featuring dispersive or anisotropic media that closely mimic real-world conditions and extending the application to various imaging techniques involving objective function minimization.

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.