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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]

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]

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]

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]

Investigation into the electromagnetic response of porous media and the porosity distribution is a key for improved porous material. However, current ground penetrating radar (GPR) simulation techniques based on the finite difference method (FDM) or even finite element method (FEM) are mainly focused on describing macro characteristics of porous property. In this regard a novel methodology combining the FEM and B-spline wavelet on the interval (BSWI) is proposed to specify the propagation characteristics of GPR echoes in a micro-perspective way. A line current radiation model is first introduced to validate the high precision and multi-scale trait of BSWI. In addition, two representative models with different porosity distributions are established using the spectral decomposition and hybrid auto-correlation functions based on the stochastic process. By using this technique, specifically, can reduce the uncertainty of model error, making the models built more in line with the actual circumstances. Next, the multi-scale technique is developed based on the scaling function using tensor product of BSWI, by which the porous model can be split into multi-resolution space both temporally and spatially. Through this integration of parameter-oriented porous modeling and multi-resolution calculation technique, a set of examples and comparisons are presented, which validates the effectiveness and reliability of the proposed methodology for analyzing complex kinematic nature of diffraction and corresponding electromagnetic characteristics to different porosity distributions. • Proposing an approach to construct the model with specifiable porosity distributions for GPR testing. • Comparing the electromagnetic response of porous media using the proposed approach with that of the finite element method. • Investigating the kinematic nature of diffracted waves under different porosity distributions. • Presenting the shape function and element matrix of the B-spline wavelet on the interval. [ABSTRACT FROM AUTHOR]

Irregular and complicated tunnel lining defects cannot be finely depicted with the standard finite difference simulation method based on regular mesh due to its irregular geometrical characteristics and complex physical properties of various defects. To solve this problem, a new hybrid algorithm is proposed for all-refined ground penetrating radar (GPR) simulation by combining the finite element time domain (FETD) and finite difference time domain (FDTD), which employs FETD algorithm in main region based on unstructured Delaunay mesh and Newmark- β implicit FDTD algorithm in PML region based on convolutional perfectly matched layer (CPML), respectively. The calculation efficiency and simulation accuracy of different algorithms methods are compared in detail using a representative numerical model with a water-filled void embedded. In addition, several complicated numerical models involving different typical defects and mixed case (e.g. non-compactness, voids, leaking with fractured and cracks, etc.) are also conducted, the results presented show that the proposed hybrid algorithm helps GPR practitioners to gain a deeper insight into the effects of multi-defects on the propagation characteristic of electromagnetic wave, and provides more precise technical guidance for defect interpretation. Moreover, spectrum analysis of echoes responses from defects, by using the short-time Fourier transform (STFT), is also presented, including amplitude spectrum, frequency spectrum and mode of dominant frequency. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) of common-offset ground penetrating radar (GPR) data can make up for the defects of the traditional prospecting technology such as destructive, cumbersome, and low efficiency, which also provides a robust and high-precision tool for quantitative assessment of subsurface prospecting. Nonetheless, conventional GPR-FWI is of limited practicality since the deficiency of accurate source wavelet and tremendous computational cost. We develop an efficient source-independent inversion algorithm based on the convolution method to eliminate the influence of source wavelet and enhance the algorithm efficiency to inverse field data. To suppress the noises induced by the convolution and cross-correlation operations, the time window is introduced in the reference trace which is selected automatically by singular value decomposition (SVD). The stochastic strategy reduces the computational cost by source subsampling. The synthetic model of a complex lens body demonstrates that source-independent FWI with time window has more tolerant to the initial model and noise, and the stochastic strategy improves the efficiency with ensured inversion accuracy. In the field GPR data of a sand trough containing two anomalous bodies, our algorithm has good adaptability and can accurately reconstruct the distribution of the permittivity and conductivity of the subsurface media without accurate estimation of the source wavelet. The results above indicate that the proposed algorithm in this study is valid for common-offset GPR data and improves the inversion efficiency by 1.24 to 3.10 times, which shows great potential to promote qualitative assessment in real-time subsurface exploration. • Develop an efficient source-independent GPR-FWI algorithm based on convolution • Automatical selections of the reference trace and time window suppress the noises • The stochastic strategy improves the inversion efficiency by 1.24 to 3.10 times • Validate the proposed algorithm through synthesis and field tests • Reconstruct details of the target body accurately without estimate of source wavelet [ABSTRACT FROM AUTHOR]

The finite element time-domain (FETD) method is an appealing electromagnetic (EM) field-solving procedure for the derivation of high-resolution field distribution of complex geological models. However, the arising computational burden has become a main bottleneck restricting the efficient implementation of ground penetrating radar (GPR) simulation at a high speed and large scale. In this research, we developed a high-efficiency EM solver to discretize the partial differential equations on unstructured triangular meshes, using a high-order graphics processing unit (GPU)-finite element discontinuous Galerkin time-domain (DGTD) method. By introducing a semi-discrete strong format instead of solving large stiffness matrices, and by employing the Runge-Kutta temporal integration scheme, the DGTD method can effectively overcome the problem of memory shortage and thereby solves the issue of instability. Besides, the uniaxial perfectly matched layer (UPML) is extended to match the lossy medium and used as an absorbing boundary condition to simulate an open space. Three models were manufactured to compare the DGTD method with the state-of-art methods (FDTD, FETD) of different grids in terms of computing accuracy, the dispersion degree of the rough interface, and memory occupied. To be specific, we explored the detailed effect of both the grid sizes and the order of basis functions on the modeling accuracy for the proposed GPU-DGTD method. We finally verified the numerical solution and demonstrated its applications by simulating a complex model for tunnel geological forecast. The experimental results reveal the order of basic function N and the size of the grid d are closely concerned with the wavelength λ of EM waves, for example, an appropriate definition is d / N ≈ λ /15. • The unstructured-grid high-order GPU-DGTD is developed for GPR. • GPU-DGTD can be applied to complex models and have high accuracy and efficiency. • d / N ≈ λ /15 is the recommended formula for grid sizes and order of basis functions. • A high-order DGTD with large grid is better than a low-order one with small grid. [ABSTRACT FROM AUTHOR]

Reverse time migration (RTM) is the important intermediate step for focusing the radar diffracted energy of the targets in ground penetrating radar imaging. The conventional RTM algorithm demands a large number of iterative trial experiments and depends on the experts' decision on the estimation of the velocity or relative permittivity of the subsurface medium. Meanwhile, the RTM profile is vulnerable to artifacts, which are composed of noise interference, multiple interferences, arc-shaped clutter, and crosstalk, so it is difficult to inspect visually. Therefore, we propose a RTM method based on accurate velocity estimation and total variation (TV) de-noising to improve the accuracy of the RTM imaging. Firstly, the appropriate migration velocity is obtained automatically by autofocusing metrics to reduce the number of visual inspection times and corrections in the migration processing. Secondly, the TV de-noising strategy based on split Bregman iterative is applied to the RTM profile with the cross-correlation imaging condition, so that the edge of the target can be obvious and the position can be accurate. Then, we apply the proposed method to the simulation data of the pipeline model and the tunnel lining model. All results show that the selected three different autofocusing metrics have unimodality and unbiasedness, which can focus on a single relative permittivity to obtain appropriate migration velocity. Furthermore, the TV de-noising strategy successfully eliminates artifacts, reconstructs contours, enhances the edge sharpness, and improves the quality and accuracy of the radar profile. Finally, we take the field data of the LiuYang River tunnel to verify the applicability of our method, we choose 2–30 as a wider range of relative permittivity based on prior information. Considering the different lateral velocity of the field data, we adopt lateral segmentation processing to improve the quality of the GPR profile. The tests of simulation data and the field data indicate that the proposed method can provide a scientific and effective way for accurate interpretation of GPR data. • Autofocusing techniques obtain the appropriate velocity model automatically. • Total variation de-noises and improves the accuracy of GPR imaging. • This strategy was successfully applied to the simulation data and the field data. • The split Bregman iterative method converges to an accurate solution. [ABSTRACT FROM AUTHOR]

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]

Accurate and efficient 3D simulation is extremely important for both grasping the features of radar wave propagation in realistic models and the high-resolution inversion. In this paper, we propose a new numerical algorithm called the low-storage five-stage fourth-order explicit Runge-Kutta interpolation scaling function method (LSERK-ISFM) and apply it into the 3D simulation of ground penetrating radar (GPR). Firstly, we derive the auxiliary differential equation (ADE) of the 3D Maxwell's equations based on the complex-frequency shifted perfectly matched layer (CFS-PML), discretize the spatial derivative of the Maxwell equation by ISFM, and integrate the temporal dimension through the LSERK scheme. Compared with the traditional finite-difference time-domain (FDTD) method, the LSERK-ISFM algorithm can effectively improve the precision of space-time dispersion of the Maxwell's equations. Specifically, by the homogeneous vacuum medium model, we compare the forward modeling results of the different number of grids per wavelength, the comparison results prove that a smaller number of grids per wavelength can be selected in the LSERK-ISFM scheme, which can greatly contribute to reducing the consumption of memory. Then we build a simple 3D model to properly compare the proposed algorithm with the coarse-mesh FDTD and the fine-mesh FDTD, the results show that, with the same mesh generation, the LSERK-ISFM algorithm outperforms the conventional FDTD algorithms in terms of both accuracy and efficiency. Finally, we test the algorithm on a 3D complex model by a series of 3D forward profiles and the corresponding slices, both simulated reflections and diffractions clearly depict the shape and location of the anomalous bodies, which shows great potential of application in the field of 3D exploration and interpretation for GPR community. • A new numerical simulation algorithm LSERK-ISFM is developed in the simulation of 3D GPR. • The LSERK method allows a larger stable time step, which in general has a better dispersion characteristic. • LSERK-ISFM methodology effectively reduces the high memory consumption and improves the simulation efficiency. [ABSTRACT FROM AUTHOR]

The conductivity distribution of subsurface often contains segmented structure with sharp interfaces, although the magnetotelluric (MT) inversions based on Tikhonov regularization are usually stable, the smoothing interfaces tomography result from high-resistance artifacts and boundary blurring still post a great challenge for MT inversion. On the contrary, Total variation (TV) regularization can deal with the problem of high-impedance artifact and boundary blurring, but its stability is difficult to be guaranteed. In order to invert the sharp interface of conductivity distribution and improve the accuracy of MT inversion, this paper proposes an MT inversion method based on a new modified total variation (MTV) regularization. The improved method transforms the TV model constraint into two different sub-constraints: one is the standard MT inversion constraint with L2 norm, and the other is the standard L2-TV model constraint, then the Gauss-Newton algorithm and split Bregman iteration are employed to separately solve these two different sub-constraint problems. In the inversion experiments, we design a wedge-shaped model and an undulating surface model, the comparison results of accuracy and efficiency show that, among Tikhonov, TV and MTV regularization methods at the same iterations, the proposed regularization method can achieve the lowest reconstruction error and data misfit, thereby eliminating high-resistance artifacts and, in particular, can solve the problem of smoothing interfaces tomography. To this end, a field data testing is also presented to verify the effectiveness and the practicability of the new modified method. • A new regularization to decompose the model constraint by transforming the TV model constraint into two sub-constraints. • The MTV regularization can solve the problem of high resistance artifacts and boundary-blurring for MT inversion. • Combining the Gauss-Newton algorithm and split Bregman iteration can present the sharp interface of conductivity distribution. [ABSTRACT FROM AUTHOR]

We present a new approach of unstructured grid adaptive finite element in the frequency domain (AFEFD) based on exact perfect matching layer (EPML) boundary condition for Ground penetrating radar (GPR) simulation. The discrete equations of transverse magnetic mode are derived by using the unstructured Delaunay mesh coupled with linear interpolation basis functions. For the trade off between the computational efficiency and the accuracy of simulation, an h-type adaptive finite element method (AFEM) is employed and, in particular, with less layer thickness and fewer parameters setting, an EPML boundary condition is developed to truncate the AFEM lattices. In addition, the Zienkiewicz-Zhu posterior error estimation, the mesh refinement scheme and the implementation process of AFEM are also elaborated in detail. To testify the validity and practicability of this methodology, a homogeneous medium model of line current radiation field with analytical solution and a complex model of lining fracture disease are considered. The results of frequency-domain slices, transformed time-domain forward profile, together with wavefield snapshots demonstrated that the reflections, diffractions and other codas caused by non-physical factors at the boundary are effectively absorbed, subsequently, the absorbing performance of simulation with AFEM in frequency domain is greatly improved. More importantly, the proposed methodology can automatically control its mesh density according to the features of the complex object, which significantly economize the computing resource of simulation when AFEM is employed. • The unstructured grid AFEFD method based on exact PML is developed in the forward modelling of GPR. • EPML makes the simulation can be performed efficiently and rapidly with less layer thickness and fewer parameters setting. • The proposed methodology can automatically control its mesh density according to the features of the complex object. • Verification of the practicability with AFEFD based on exact PML in complex medium. [ABSTRACT FROM AUTHOR]

Complex irregular defects of tunnel linings under complicated geological conditions cannot be accurately reconstructed with the traditional full-waveform inversion (FWI) method due to their irregular geometrical characteristics and complex dielectric properties. Because of extensive inversion calculations and high memory requirements, the traditional FWI method is very sensitive to the initial model and plunge into a local minimum or cycle skipping. To solve this problem, a novel ground-penetrating radar (GPR) FWI method involving two parameters (i.e. permittivity and conductivity) is proposed for improving reconstruction accuracy of lining defects using the total variation (TV) regularization. First, the Delaunay unstructured triangular mesh in finite element time-domain (FETD) method is employed to perform GPR forward modeling, and then the total-variation model constraint and multi-scale inversion strategy are implemented during execution of the conjugate gradient (CG) algorithm, which facilitates the quick search for the global optimal minimum value, thus guaranteeing the avoidance of the ill-posed problem during the inversion process. Accordingly, the detailed features of lining defects can be characterized and reconstructed even for those complicated geological conditions, more specifically, the results show that, with fewer iterations (up to 59 times less), the proposed method present a lower reconstruction error both on permittivity (up to 12.05% lower) and conductivity (up to 7.35% lower). From a comparison of the inversion results and the model, it can be concluded that the proposed FWI algorithm can effectively eliminate the non-physical oscillation and artifacts in the image reconstruction, which may significantly improving the accuracy of defects interpretation and assessing the severity of complex defects. [ABSTRACT FROM AUTHOR]

Tailings produced by mining engineering and metal smelting industries have become a major challenge to the ecological environment and human health. Environmental compatibility, mechanical stability, and economic feasibility have restricted the treatment and reuse of tailings. A novel solidification/stabilization technology using hydantoin epoxy resin (HER) and red clay for copper tailing treatment was developed, and the leaching behaviors of solidified/stabilized copper tailings were investigated in this paper. The leaching characteristics were analyzed by toxicity characteristic leaching procedure (TCLP) leaching tests. Besides, the influence of red clay content and acid rain on the permeability characteristics and leaching characteristics were investigated based on flexible-wall column tests and microstructure tests. The results showed that the copper tailings solidification/stabilization technology with HER and red clay had excellent performances in toxicity stabilization. The leaching concentration of Cu in TCLP tests and flexible wall column tests remained within the limit specified by the Chinese national standard, and the concentration of Cu decreased significantly with the increase of the red clay content. Moreover, acid rain leaching changed the mineral composition and microstructure of solidified tailings, and the porosity of the samples increased with the dissolution of soluble minerals. Additionally, the hydraulic conductivities decreased slightly with the increase in the pH value of acid rain, and the solidified sample with 5% red clay had the lowest hydraulic conductivity. [Display omitted] • A novel solidification technology was proposed for tailings disposal. • Leaching behaviors of solidified tailings was studied with TCLP tests. • The influences of acid rain were investigated based on column tests. • Acid rain changed the mineral composition and structure of solidified tailings. [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]

An integrated geophysical investigation was performed at S dam located at Dadu basin in China to assess the condition of the dam curtain. The key methodology of the integrated technique used was flow-field fitting method, which allowed identification of the hydraulic connections between the dam foundation and surface water sources (upstream and downstream), and location of the anomalous leakage outlets in the dam foundation. Limitations of the flow-field fitting method were complemented with resistivity logging to identify the internal erosion which had not yet developed into seepage pathways. The results of the flow-field fitting method and resistivity logging were consistent when compared with data provided by seismic tomography, borehole television, water injection test, and rock quality designation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Novel SPR-HPLC–MS/MS method for screening of HSA binders was proposed. [•] The developed method was simple, effective and high-throughput. [•] Twenty bioactive components in Radix Astragali were identified. [•] Comprehensive mass fragmentation pathways were established. [Copyright &y& Elsevier]

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]