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Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images. [ABSTRACT FROM AUTHOR]

Aiming at solving the clutter flooding problem in the traditional cross correlation reverse time migration (RTM) of ground penetrating radar (GPR), we proposed an improved RTM method based on multitraces cross correlation window (MCW) imaging condition. The main difference between the proposed method and the traditional direct stacking is that it can effectively enhance the effective signal while weakening the clutter by performing MC calculation on the single trace imaging results, avoiding the enhancement of both clutter interference and effective signal concurrently by direct stacking. Second, the window threshold is set according to the GPR observation accuracy, and the effective signal in the MC result is retained as the abnormal region window, while the imaging results in the nonabnormal region are discarded, so as to suppress the clutter and retain the abnormal region information. Numerical experiments show that compared with the traditional RTM and total variation (TV) denoising method with cross correlation imaging conditions, the MCW imaging condition can accurately locate abnormal region, suppress clutter interference, and have the advantage of no loss of effective information, which greatly improves the imaging quality. Finally, the proposed method is applied to the measured data to verify the practicability and effectiveness in practical engineering applications. [ABSTRACT FROM AUTHOR]

The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model. However, it faces problems of crosstalk between multiple parameters and strong nonlinearity. This research proposes a multi-constrained, multi-parameter FWI framework based on the projected quasi-Newton algorithm. This framework can introduce multiple types of prior geological information, which can effectively improve the problem of multi-parameter inversion. Additionally, the quasi-Newton method can eliminate the crosstalk phenomenon to further improve the inversion convergence speed. Taking the 1994BP model as an example, the results show that the projected quasi-Newton method has a faster convergence speed than the spectral projected gradient method, and reduces the crosstalk between parameters; multiple constraint sets are uniquely projected onto the intersection to ensure that the estimated values of model parameters meet multiple constraints. We also experiment with the overthrust model, which shows that the framework we proposed can improve the inversion accuracy and has good adaptability. The proposed multi-parameter inversion framework can be compatible with more prior information to obtain an inversion model that conforms to geological understanding and shows great potential in seismic exploration. [ABSTRACT FROM AUTHOR]

A reliable inspection of the tree trunk internal defects is often considered vital in the health condition assessment for the living tree. There has been a desire to reconstruct the internal structure quantitatively using a non-destructive testing technology. This paper intends to apply wavefield reconstruction inversion (WRI) to obtain high-precision information from tree trunk detection using ground penetrating radar data. The variational projection method and the grouped multi-frequency strategy are adopted to strengthen the algorithm stability and adaptability by inverting frequency components sequentially. Through an irregular trunk model test, the influence of the penalty parameter, initial model, frequency strategy, and grid generation methods are investigated on WRI. Additionally, the comparison between full waveform inversion and WRI is discussed in detail. This synthetic case indicates that WRI is efficient and for a reasonable result, a proper multi-frequency strategy and an accurate mesh closer to reality are important. Furthermore, a field case of a historical tree is used to prove the validity and reliability of the algorithm. The success in this case indicates that our algorithm can characterize the distribution of media parameters of tree trunks accurately, which could provide data support for the rejuvenation and maintenance of living trees. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR), geophysics exploring technology, could non-destructively acquire high-precision information about the shallow subsurface. This Special Issue aims to introduce and report on recent advancements in GPR, encompassing aspects such as numerical simulation, inversion imaging, data processing methodologies, and various domains of GPR application. In the modern epoch, equipment for GPR data acquisition is well established, but the explication of GPR data still relies on artificial judgment and experience, lacking automation. [Extracted from the article]

Magnetotellurics (MT) is an important geophysical method for resource exploration and mineral evaluation. As a direct and effective form of data interpretation, MT inversion is usually considered to be a penalty-function constraint-based optimization strategy. However, conventional MT inversion involves a large number of calculations in penalty terms and causes difficulties in selecting exact regularization factors. For this reason, we propose a multiplier-based MT inversion scheme, which is implemented by introducing the incremental Lagrangian function. In this case, it can avoid the exact solution of the primal-dual subproblem in the penalty function and further reduce the sensitivity of the regularization factors, thus achieving the goal of improving the convergence efficiency and accelerating the optimization calculation of the inverse algorithm. In this study, two models were used to verify the performance of the multiplier method in the regularized MT inversion. The first experiment, with an undulating two-layer model of metal ore, verified that the multiplier method could effectively avoid the MT inversion falling into local minimal. The second experiment, with a wedge model, showed that the multiplier method has strong robustness, due to which it can expand the selection range and reduce the difficulty of the regularization factors. We tested the feasibility of the multiplier method in field data. We compared the results of the multiplier method with those of conventional inversion methods in order to verify the accuracy of the multiplier method. [ABSTRACT FROM AUTHOR]

High-precision detection of the underground pipelines is an indispensable part of the development and construction of cities. At present, the inversion technology for ground-penetrating radar (GPR) data is an effective means of realizing shallow-underground-space visualization in the field of geophysical exploration. However, the traditional full-waveform inversion (FWI) method usually faces the problems of strong nonlinearity of the objective function, high dependence on the initial model, and huge calculation cost. For improving the accuracy and efficiency of GPR data inversion, a wavefield reconstruction inversion (WRI) strategy is used for GPR data imaging to reduce the nonlinearity of the inversion problem and the dependence on the initial model. Then, the frequency weighting strategy and the multi-scale method are adopted to avoid the high-frequency component data dominating the optimization process and enhance the stability of inversion. In this paper, two numerical experiments of pipeline models with different materials and spacing or buried depths verified that the proposed method can effectively reconstruct the subsurface pipelines, and further performance of our algorithm on the field data verified the reliability of high-precision imaging of urban underground pipelines, which shows great potential of application in the field of high-precision detection of the urban underground pipelines. [ABSTRACT FROM AUTHOR]

The full-waveform inversion (FWI) of ground-penetrating radar (GPR) data yields promise for quantitatively characterizing the parameters of the Earth’s shallow subsurface. However, conventional FWI is highly nonlinear and suffers from cycle skipping once the low-frequency data are missed or the initial model is poor. Furthermore, having limited prior knowledge of the subsurface in GPR measurements increases the ill-posedness of the inverse problem. Wavefield reconstruction inversion (WRI), which mitigates cycle skipping, extends the FWI search space by relaxing the wave equation constraint, reduces the nonlinearity, and is less sensitive to the initial model. In this article, we extend WRI to the 2-D frequency-domain imaging of on-ground GPR data. To improve the inversion stability and mitigate the ill-posedness, we utilize modified total variation (MTV) regularization to constrain the inverted models. With a simple numerical example, we first investigate the effects of the penalty parameter, initial models, and MTV regularization on WRI and further discuss the differences between WRI and FWI. Then, we analyze the sensitivity of the proposed approach to the frequency component and noise in a multiple-targets model. Furthermore, we assess our method with a complex synthetic example containing noise-contaminated data, showing that our proposed approach works efficiently even given noisy GPR data. Therefore, the reasonable combination of WRI and MTV regularization can improve the accuracy and efficiency of imaging for on-ground GPR data. This joint approach for the multiparameter quantitative reconstruction of GPR data ultimately exhibits good applicability and strong robustness and is worthy of promotion. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) full-waveform inversion (FWI) is an emerging near-surface high-resolution imaging technology that has been widely used in GPR 2-D imaging to better quantitatively evaluate the permittivity and conductivity of the subsurface. However, the 2-D imaging method limits the applicability and accuracy of FWI for the 3-D object cases. In this letter, we develop a 3-D FWI algorithm involving permittivity and conductivity for GPR in the frequency domain. The method employs the edge-based finite element method to solve the forward problem and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method to solve the inverse problem, which avoids the calculation of the Hessian matrix. In particular, by using the parameter scaling and scaling factor, it is convenient to carry out the simultaneous inversion of permittivity and conductivity with high efficiency. In addition, a modified total variation regularization scheme is utilized for ensuring the stability of the inversion, as well as identifying the abnormal body boundary more effectively. As a demonstration, a synthetic 3-D model experiment is presented to test the proposed algorithm. The results show that the proposed inversion strategy can accurately reconstruct the permittivity structure together with the conductivity distribution from the 3-D on-ground GPR data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To address problems, such as the computationally intensive inversion requirements, low inversion efficiency, and inadequate inversion accuracy caused by multiparameter crosstalk in a synchronous inversion, a new dynamic stochastic source encoding strategy combined with minmax-concave total variation (MCTV) regularization model constraints was proposed. This strategy avoids crosstalk noise between shots caused by the algorithm and greatly improves the inversion efficiency without affecting the inversion accuracy. By comparing a “cross”-shaped model with the multiparameter inversion results, we found that the MCTV regularization strategy boasts the best inversion effect. We further showed that dynamic stochastic source encoding can increase the inversion efficiency threefold by applying the 1994 British Petroleum (BP) migration international standard topography model and establishing a function to evaluate the most efficient inversion strategy from among seven options. Compared with the traditional stochastic source encoding strategies, dynamic stochastic source encoding was shown to better suppress crosstalk noise. The proposed strategy also presented a higher acceleration ratio; additionally, combining this strategy with MCTV regularization model constraints provided the clearest reconstructed image with the highest inversion precision and obtained the best evaluation score among the considered inversion strategies, albeit with a slight reduction in the total elapsed time-acceleration ratio. [ABSTRACT FROM AUTHOR]

Cadzow filtering is currently considered as one of the most effective approaches for seismic data reconstruction. The basic version of Cadzow filtering first reorders each frequency slice of the seismic data (to be reconstructed) to a block Hankel/Toeplitz matrix, and then implements a rank‐reduction operator, that is truncated singular value decomposition, to the Hankel/Toeplitz matrix. However, basic Cadzow filtering cannot deal with the problem of recovering regularly missing data (up‐sampling) in the case of strongly aliased energy, because the regularly missing data will mix with signals in the singular spectrum. To solve this problem, it has been proposed to precondition the reconstruction of high‐frequency components using information from the low‐frequency components which are less aliased. In this paper, we further extend the de‐aliased Cadzow filtering approach to reconstruct regularly sampled seismic traces from the noisy observed data by modifying the reinserting operation, in which the high‐frequency components are projected onto the sub‐space spanned by several singular vectors of the low‐frequency components. At each iteration, the filtered data are weighted to the original data as a feedback. The weighting factor is related to the background noise level and changes with iteration. The feasibility of the proposed technique is validated via two‐dimensional, three‐dimensional and five‐dimensional synthetic data examples, as well as two‐dimensional post‐stack and three‐dimensional pre‐stack field data examples. The results demonstrate that the proposed technique can effectively interpolate regularly sampled data and is robust in noisy environments. [ABSTRACT FROM AUTHOR]

For magnetotelluric (MT) data with a large coverage, inversion interpretation for the whole area is usually impossible, due to the physical limitation of a computer. However, for each MT station, it is only sensitive to a region (called MT footprint) localized immediately underneath the station, much smaller than the whole survey area. Thus the whole large survey area can be subdivided into many sub-areas and inverted using the moving footprint technique efficiently. Therefore, to understand the MT footprint is of great importance for developing such an inversion algorithm. In this paper, we study the influence of frequency, resistivity and existence of anomalous structure in the MT footprint. We first use the finite difference method to evaluate the total electromagnetic (EM) fields, then apply the numerical volume integration of tensor green's function to calculate the total induced fields at the receiver (summation of the contribution for each cell). The footprint is obtained based on the contribution of each cell to the total induced fields. Different models are designed to understand the footprint for MT problems. An increase in frequency or conductivity results in a decrease in the size of footprint. Existence of anomalous bodies within the footprint affects the MT footprint size to a degree, dependent on the overall resistivity. Outside the footprint, the existence of anomalous bodies have little effect on the footprint size and shape. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The downdip limit of seismogenic interfaces inferred from the subduction thermal regime by thermal models has been suggested to relate to the faulting instability caused by the brittle failure regime in various plate convergent systems. However, the featured three‐dimensional thermal state, especially along the horizontal (trench‐parallel) direction of a subducted oceanic plate, remains poorly constrained. To robustly investigate and further map the horizontal (trench‐parallel) distribution of the subduction regime and subsequently induced slab dewatering in a descending plate beneath a convergent margin, we construct a regional thermal model that incorporates an up‐to‐date three‐dimensional slab geometry and the MORVEL plate velocity to simulate the plate subduction history in Hikurangi. Our calculations suggest an identified thrust zone featuring remarkable slab dehydration near the Taupo volcanic arc in the North Island distributed in the Kapiti, Manawatu, and Raukumara region. The calculated average subduction‐associated slab dehydration of 0.09 to 0.12 wt%/km is greater than the dehydration in other portions of the descending slab and possibly contributes to an along‐arc variation in the interplate pore fluid pressure. A large‐scale slab dehydration (>0.05 wt%/km) and a high thermal gradient (>4 °C/km) are also identified in the Kapiti, Manawatu, and Raukumara region and are associated with frequent deep slow slip events. An intraslab dehydration that exceeds 0.2 wt%/km beneath Manawatu near the source region of tectonic tremors suggests an unknown relationship in the genesis of slow earthquakes. [ABSTRACT FROM AUTHOR]

We introduce a novel implementation of a rotated staggered grid (RSG) finite difference (FD) method based on the unsplit convolutional perfectly matched layer (UCPML). The method is carried out using rotated staggered FD operators and a general complex frequency shifted stretching factor. It can offer a number of advantages over the RSG with split convolutional perfectly matched layer (SCPML), such as improving computational efficiency, overcoming the numerical dispersions resulting from the high-frequency propagation and strong contrasts in material, and specifically absorbing the evanescent modes at grazing incidence. A half-space numerical example with a lossy medium attached demonstrates that large-scale spurious reflections and evanescent modes at grazing incidence are absorbed efficiently by the UCPML in RSG scheme. Meanwhile, the comparison of the reflection error between the proposed algorithm and traditional approaches [using time-dependent reflection error (TDRE) and frequency-dependent reflection error (FDRE)] suggests that the proposed algorithm of RSG provides a preferable improvement in absorbing low-frequency coda wave of evanescent modes, particularly in the lossy medium that possesses inhomogeneous discontinuities. [ABSTRACT FROM PUBLISHER]

The high-fidelity attenuation of random ground penetrating radar (GPR) noise is important for enhancing the signal-noise ratio (SNR). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuation process, including overfitting, the size of the local receptive field, and representational bottlenecks and vanishing gradients in deep learning; this approach also significantly improves the noise attenuation performance. We described the noise attenuation process of conventional CDAEs, and then presented the output feature map of each convolutional layer to analyze the role of convolutional layers and their impacts on GPR data. Furthermore, we focused on the problems of overfitting, the local receptive field size, and the occurrence of representational bottlenecks and vanishing gradients in deep learning. Subsequently, a network structure optimization strategy, including a dropout regularization layer, an atrous convolution layer, and a residual-connection structure, was proposed, namely convolutional denoising autoencoders with network structure optimization (CDAEsNSO), comprising an intermediate version, called atrous-dropout CDAEs (AD-CDAEs), and a final version, called residual-connection CDAEs (ResCDAEs), all of which effectively improve the performance of conventional CDAEs. Finally, CDAEsNSO was applied to attenuate noise for the H-beam model, tunnel lining model, and field pipeline data, confirming that the algorithm adapts well to both synthetic and field data. The experiments verified that CDAEsNSO not only effectively attenuates strong Gaussian noise, Gaussian spike impulse noise, and mixed noise, but it also causes less damage to the original waveform data and maintains high-fidelity information. [ABSTRACT FROM AUTHOR]

One of the great challenges of unconfined seepage through a dam lies in the accurate determination of free surface that depends on the complexity of the seepage model, especially if the model is characterized with complex geometry and sharp variations in permeability distribution. This study presents a practical methodology that combines the adaptive moving-mesh algorithm and the Galerkin finite element method (FEM) to solve an unconfined seepage problem with high efficiency and precision. The methodology employs a set of improvement terms, such as remainder factor, step-size parameter and termination condition, all of which guarantee that the simulation and the refinement fitting can be implemented efficiently until the free surface converges within a given allowable error. In particular, a specialized discussion is presented for the significant relation between the location of the exit point and the corresponding grid fineness. To validate the practicability of the proposed method, a series of examples are performed. Comparing the result with those of other numerical approaches, we conclude that even though the unconfined seepage model may be complicated with arbitrary complex geometry and sharp variations in permeability distribution, the proposed algorithm provides a great improvement in efficiency and accuracy in free-surface searching. [ABSTRACT FROM AUTHOR]