In the ground-penetrating radar (GPR) detection of concrete structures, the reflection of rebar layers often obscures the useful signals below. In this letter, an effective and practical method for removing rebar clutter is proposed. It is based on iterative F-k migration and demigration, combined with real-time mask window and some classic GPR data processing steps. First, we calculate the wave velocity through travel time and layer thickness and migrate the B-scan data. Then, we create a mask window to extract the focused rebar reflection. Finally, the rebar clutter is restored through F-k demigration and removed from the original data. Meanwhile, multiple iterations are performed to ensure the complete removal of rebar clutter. The proposed method is not limited by data size and observation scale. The effectiveness of the proposed method is demonstrated by both numerical simulations and model field experiments.

Above surface diffraction (ASD) is a common interference in ground penetrating radar (GPR) data caused by the reflection of above surface objects. It is particularly severe in low-frequency unshielded and drone GPR data. Existing ASD removal algorithms rely heavily on manual adjustments, and the ASD need to be empirically identified in advance to manually construct the reference window. In this paper, we propose a fully automatic ASD removal method that eliminates manual operation. The proposed approach combines Stolt migration with the extractor based on Generative Adversarial Networks (GAN), which can enable the automatic removal of ASD from GPR data. Firstly, we focus the ASD events in GPR data by performing Stolt migrating with the electromagnetic wave velocity in the air. Secondly, we train a high-resolution GAN to extract the focused ASD clusters. After the alignment of energy and scale, the extracted ASD clusters are demigrated back into the x-t domain. Finally, we remove the ASD from the raw data via multiple filters. Our fully automatic workflow requires no manual parameter settings or user intervention, ensuring practical usability for diverse real-world scenarios. Subsequent to extensive numerical experiments and practical validation, including UAV-based GPR surveys, environments with high-voltage powerline interference, and the Jiaojia archaeological dataset, our method found to be effective, stable and robust.

The distribution and hydrological characteristics of permafrost play a crucial role in environmental stability. Ground-penetrating radar (GPR), as a non-invasive geophysical method, can effectively describe the layer structure of permafrost and obtain permittivity distribution through inversion. However, gradient-based full waveform inversion (FWI) poses significant computational challenges for large-scale GPR inversion, particularly in three-dimensional (3D) permafrost models. While deep learning offers a computationally efficient alternative, existing methods struggle to account for variations in field source wavelets. To address these limitations, we propose a deep learning-based, source-independent FWI approach for rapid GPR inversion. Our method leverages a cross-convolution strategy, where reference traces containing field source wavelets are convolved with simulated responses during training. During prediction, simulated standard wavelets are convolved with field data, ensuring data consistency between the training and inference stages. Meanwhile, we specifically designed a source-independent inversion network that integrates a forward-cycle head mechanism and a near-field interference loss. This approach enhances robustness against non-standard wavelets, making it particularly suitable for field applications. In order to ensure the consistency with real-world conditions, we construct a simulation dataset under 3D scene for training, and further discuss the differences between 2D and 3D GPR simulations. Finally, we apply the proposed method to GPR data from three sites in the Tanggula Mountains on the Tibetan Plateau. The results provide quantitative assessments of ice content in permafrost and water content in thawed zones, which align well with borehole data.

In ground-penetrating radar (GPR) data, clutter and noise are commonly observed in B-scan images, which can seriously affect the interpretability of the GPR data. In this article, we propose wavelet-GAN, a deep-learning network that integrates generative adversarial network (GAN) and discrete wavelet transform (DWT). Wavelet-GAN could decompose the GPR image into multiple frequency subimages and remove clutter. Additionally, we solve the problem of error handling when there are no features in the dataset through micro datasets, dataset fine-tuning, high-speed training, and multiple feature generalization. Our method decomposes GPR image by DWT, then convolutional neural network (CNN) and GAN are, respectively, used to reconstruct low-frequency and high-frequency target signal information. Finally, the information of different frequency bands is combined into a new GPR image by inverse DWT (IDWT). Wavelet-GAN uses a small-scale dataset for training, which enables it to make rapid adjustments to process new target types, even if we only have one typical target data. We compare our method with traditional methods and other deep-learning based methods and demonstrate that our wavelet-GAN performs better in real data processing. Finally, we apply this method as a data-preprocessing tool for machine learning inversion and tested its feasibility.

Late-time negative responses in central-loop transient electromagnetic (TEM) data are often linked to the induced polarization (IP) effect. Early methods for modeling the IP effect in TEM data try to avoid calculating the fractional derivative arising from considering the Cole-Cole model by either using the Fourier transform to convert frequency-domain responses to the time domain or approximating the fractional derivative in the time domain directly. The frequency-to-time conversion method suffer from accuracy issues if the number of frequencies calculated is small. The time-domain approximation method also has accuracy issues because of simplified Cole-Cole models. The Caputo series can approximate fractional derivatives accurately if historic electromagnetic (EM) fields are saved. However, the storage of historic EM fields leads to a significant memory consumption. We introduce the sum-of-exponentials (SOE) method to discretize fractional derivatives, which does not need to store field values from previous times except for the first two time-steps. We discretize the resulting partial differential equations from the SOE discretization using a finite-difference time-domain (FDTD) approach. Additionally, we improve computational efficiency by employing the direct-splitting strategy to transform large sparse matrices into smaller diagonally dominant tridiagonal matrices. We validate the accuracy and efficiency of our algorithm by comparing it with the Caputo approximation method using a chargeable half-space model. Furthermore, we compare our results with existing literature data for a chargeable anomaly in a nonchargeable half space. Finally, we analyze the response characteristics of the IP effect using a block-in-half space model.

Controlled-source electromagnetic (CSEM) surveys are widely used, but due to the influence of instrumental or environmental factors, the data obtained from CSEM surveys is often disturbed by noise. To address this problem, a de-noising method based on least-squares inversion has been proposed to effectively deal with the non-periodic noise in CSEM data. This method selects special reconstruction areas in the time domain and establishes an over-determined equation to obtain the noise. However, the condition in selecting reconstruction areas is rigorous, according to which only an area merely consists of signal and white Gaussian noise can be chosen as an ideal reconstruction area, limiting the application of this method. In this paper, an improved method was proposed by introducing Legendre polynomials into the over-determined equation. Using this improved method, areas containing other kinds of noise can also be chosen as reconstruction areas, extending the scope of application. To avoid the possible calculation error that comes from an ill-conditioned matrix, a regularization factor is introduced into the over-determined equation as an option. Meanwhile, to quantitatively evaluate the effect of this proposed method, the envelope evaluation method is involved and a "collapse algorithm" is proposed to evaluate the de-noising effect through residual noise. Through simulation and real case study, the validity and effectiveness of the proposed de-noising and evaluating process are proved.