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Ground penetrating radar (GPR) full-waveform inversion (FWI) can make full use of kinematics information and dynamics information to achieve the highest theoretical resolution, serving as a promising tool for reconstructing subsurface structures and the physical properties of the medium. However, conventional FWI is constrained by strong nonlinearity, easily falls into the local minimum, and requires multiple forward simulations coupled with intensive adjoint wavefield calculations, which cannot satisfy the requirements of engineering exploration. To mitigate the nonlinearity of the inversion and improve computational efficiency, this article designs an FWI framework based on deep learning, featuring a multifrequency and multiscale fusion strategy. Utilizing a multioutput convolutional neural network (CNN) constructed by the hybrid dilated convolution (HDC), the receptive field is expanded without incurring additional computational complexity and memory consumption. The dilated CNN predicts multiple sets of available low-frequency data from its respective higher frequency components of GPR data and integrates the multifrequency strategy to guide FWI to converge the global minimum. The sizes of computational models are selected according to distinct electromagnetic wave frequencies, and the very deep super-resolution (VDSR) model facilitates the automatic mapping of grids at different scales, which reduces unnecessary calculation and boosts inversion efficiency. The synthetic and field cases prove that the proposed framework significantly enhances the spatial resolution, robustness, and efficiency of FWI. The dilated CNN and VDSR constructed have demonstrated robust generalization and noise tolerance abilities, which are suitable for geophysical tasks.

In audio-magnetotellurics (AMT) inversion, the resistivity model derived from data is crucial for understanding geological properties. Current AMT inversion methods such as Gaussian–Newton (GN) and nonlinear conjugate gradient (NLCG) have limitations, including sensitivity to data errors and reliance on initial models, leading to nonuniqueness and slow convergence. To address these issues, we propose an AMT inversion framework incorporating borehole data and geological constraints. By leveraging borehole information and considering geological patterns, we develop three machine learning data construction methods that enhance the stability and speed of the inversion process. However, borehole data acquisition is costly and limited, and it represents geological properties discretely within a narrow range. Relying solely on it or unconstrained inversion can compromise accuracy. Our approach integrates borehole data into the supervised descent method (SDM) inversion, resolving data gaps and model variations. SDM results are then used as initial models for GN inversion. Synthetic and field data examples demonstrate the efficiency and feasibility of our framework, showing rapid convergence and high-quality results. This approach accelerates AMT inversion by effectively using borehole information, providing a practical solution for improving the process.

ABSTRACT 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 [...]

Multiparameter elastic full waveform inversion (FWI) makes full use of the dynamic and kinematic information of all seismic wavefield. Through the mutual constraint and verification of the three parameters of P-wave velocity, S-wave velocity, and density, the joint evaluation is carried out, which is helpful to understand the structural and lithologic information of underground media more comprehensively. The bottleneck restricting the multiparameter FWI is the large amount of calculation and low efficiency. To improve this problem, multiple shots are directly superimposed to form super shots. While it usually results in an unstable inversion due to that a large amount of crosstalk noise will be easily generated between adjacent shots. In this article, we introduce the random source-encoding strategy to improve the inversion efficiency and load the total-variation (TV) regularization term to suppress the crosstalk noise, but it also brings the problem of regularization parameters selection for multiparameter FWI. Thus, the projection method is applied to directly load the regularization term into the model as a constraint, which avoids the unsatisfactory results caused by the improper selection of regularization parameters and effectively improves the ill-posedness of inversion. Finally, three examples of the graben, the 1994BP, and the overthrust model are used to prove that the proposed algorithm based on random source-encoding and projection regularization can effectively improve the inversion efficiency, suppress noise, and has good practicability and adaptability.

Reverse time migration (RTM) has the advantage of high-precision imaging, and it can converge the radar wave back to its actual position, making it widely used in radar exploration. However, there are artifacts, low-frequency noise, and fuzzy deep imaging in RTM results. Researchers have proposed full wavefield separation imaging condition and total variation (TV) technique, both of which could suppress noise and artifacts. However, the original wavefield separation method was considerably limited by its extensive calculation, and it cannot solve the problem of weak energy of imaging in the deep zone; the conventional TV technique was likely to be affected by artifacts due to the inevitable oversmoothing suppression of anomaly edges. To address these issues, this article improves the RTM methodology by combining an optimized full wavefield separation based on Poynting vector imaging condition and TV-L1-based artifacts suppressing technique. Specifically, the physical significance of the Poynting vector is introduced to separate the wavefield for reducing the calculation burden, the compensation function is integrated with the imaging condition to compensate for the deep energy, and the TV-L1-based artifacts suppressing method is used to resolve the imaging problem of loss of specific and edge details. Synthetic data and laboratory data experiments are carried out to verify the effectiveness and practicability of the proposed RTM methodology.

Ground penetrating radar (GPR) is an important shallow electromagnetic nondestructive detection technology. The full waveform inversion (FWI) of GPR data utilizes all information, including dynamics and kinematics, theoretically has the highest imaging accuracy, and meets the increasingly sophisticated needs of engineering exploration imaging. However, the bottleneck restricting the FWI is the low calculation efficiency, which cannot meet the requirements of rapid reconstruction of underground medium in actual engineering. In order to improve the calculation efficiency, we introduce the data encoding into the GPR dual-parameter FWI. Data encoding often brings crosstalk noise, and the noise is closely related to the encoding methods and data types. For this reason, we select the encoding of the crosshole data, wide-angle reflection and refraction data, and common-offset data for inversion. The experiments show that data encoding can effectively reduce computing time, and three different GPR data require different encoding methods due to their different redundancies. Total variation (TV) regularization can suppress the noise caused by data encoding. Although it will slightly increase the calculation time, it can significantly improve the inversion quality.

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

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. A remarkable interface slab dehydration band along Kapiti‐Manawatu‐Raukumara (KMR) is identified beneath Hikurangi by 3‐D thermal modelingInterface ordinary earthquakes, tectonic tremors, and deep SSEs beneath Hikurangi are found actively occurring along KMRPlate interface beneath Manawatsu where tectonic tremors occurred is associated with high intraslab temperature gradient and high slab dehydration

There would be widespread congestion when vehicle accidents occur in city river-crossing tunnels, which are usually crucial parts in city road network. Great gradient change at the lowest point of the tunnel makes it more difficult to control the vehicles if the drivers have to make a turn at the same time. This paper researched on the safety of the lowest point of city river-crossing tunnels by building the simulated tunnel scenes through UC-win/Road software and exploring the heart rate of drivers in different conditions through the driving simulator experiments. Controlling variables method was adopted to establish the simplified models and the considered variables were longitudinal grade and radius of horizontal curve. Based on the heart rate data around the time vehicles passing through the lowest point, we defined two new physiological indexes in this paper——the variability of maximum heart rate and the variability of average heart rate to measure the drivers’ physiological load. The driving simulator experiment discovered that the two physiological indexes both had positive correlation with the longitudinal grade while they had negative correlation with the radius of horizontal curve. We also found that there was no big difference of the drivers’ heart rate between the situations with design speed of 60km/h and 40km/h. At last, we evaluated the South Xizang Road Tunnel and the conclusion was that its lowest point was safe for driving.