Theoretical advancements in full waveform inversion (FWI) of ground-penetrating radar (GPR) data have shown promising potential for enhancing the accuracy of GPR data interpretation. However, the widespread implementation of FWI faces significant challenges due to its low-computational efficiency and high memory consumption, primarily attributed to the gradient operation stage. To address these issues, we propose a spatiotemporal optimization approach for GPR FWI based on super-resolution (SR) technology. The proposed method focuses on three optimization directions: adopting a storage strategy that only preserves the forward wavefield while synchronizing the gradient operation and adjoint wavefield operation, compressing the time dimension of the GPR wavefield based on the Nyquist sampling law, and obtaining a fuzzy gradient in the spatial dimension by sampling the wavefield at each moment and restoring it using an SR network to complete the FWI. Experimental results demonstrate that the proposed optimization method achieves a nearly 50% acceleration in computational efficiency without compromising the original inversion architecture. Moreover, it reduces the memory usage to approximately 4.17% of the original memory, while maintaining the effectiveness of the inversion process. This method exhibits practicality and effectiveness through several numerical and measured data experiments, providing a solid foundation for the widespread application of FWI on commonly available microcomputers.

Reverse time migration (RTM) is an important technology for imaging ground penetrating radar (GPR) data. To address the problem of artifacts flooding of imaging results and high memory consumption of RTM, we propose an optimized multitraces cross correlation window (MCW) to increase the order of magnitude difference between the signals and artifacts for more obvious separation effect, but it also exacerbates the problem of computational cost. With the high sampling rate and high efficiency of collection method, common offset GPR is convenient to acquire large amounts of data, which consumes more numerous cost for RTM. Due to the attenuation property of high-frequency radar waves, most of the signals of common offset GPR originate from a small region below the antenna. Inspired by the footprint in airborne electromagnetic method, we propose the finite domain (FD) strategy, which limits the calculation of single trace to FD, and combine it with optimized MCW. It can reduce the computational cost of RTM and MCW significantly at the same time, especially for long profile data. Numerical experiments show that the FD reduces the computation by 77.22% with speedup 11.01. The optimized MCW retains the effective information separated from artifacts. The migration of the measured data proves the advantages and practicality of this method in engineering practical exploration.

Ground penetrating radar (GPR) migration is a crucial imaging method to obtain the spatial position, size, and shape of the underground structures. However, Kirchhoff migration, finite-difference migration, F-K migration, and reverse time migration (RTM) focus on geometric structure imaging and cannot provide realistic reflection coefficients. Least-squares reverse time migration (LSRTM) regards imaging as an inversion problem in the sense of least squares. It continuously corrects the imaging results by minimizing the residual between the simulated data and the observed data to obtain realistic reflection coefficients. In order to enhance the accuracy of the LSRTM result, we introduce the cross-correlation window to suppress artifacts and noise. Although the non-interface information in the gradient is effectively suppressed, the cross-correlation window will cause new noise to appear. This makes the LSRTM result unsatisfactory because the window is used multiple times in the calculation. Therefore, we proposed the improved cross-correlation window that utilizes the Block-matching and 3D Filtering (BM3D). This improvement preserves the ability of eliminating artifacts while preventing the window from introducing new noise. Experiments results with the synthetic data and the measured data demonstrate that compared with the traditional methods, the LSRTM based on the improved cross-correlation window suppresses noise, reduces artifacts, enhances clarity of the interfaces, and achieves higher imaging accuracy.

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 multi-traces 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. Secondly, 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 non-abnormal 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 de-noising 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.

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 over-smoothing-suppression of anomaly edges. To address these issues, this paper 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; 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.

Multi-parameter 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 multi-parameter 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 paper, 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 multi-parameter 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.

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.

A new technique for clutch self-calibration for automated mechanical transmission system based on single-axle parallel hybrid city bus is proposed. A vehicle equipped with this technique, will automatically calibrate the position of the half-engaged point of its clutch when it starts up. This calibration will eliminate or compensate the clearance due to wearing and temperature difference, and provide accurate parameters for transmission control unit, which transmission control unit can make the clutch acting rapidly and accurately, and improve the smoothness of launching and power performance, as well as shifting quality. Test results indicate this method is feasible and effective.