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. However, the majority of current GPR data inversions 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 paper proposes a full-waveform inversion 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 multi-frequency strategy and total-variation 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.

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 wave field while synchronizing the gradient operation and adjoint wave field operation, compressing the time dimension of the GPR wave field based on the Nyquist sampling law, and obtaining a fuzzy gradient in the spatial dimension by sampling the wave field at each moment and restoring it using a 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.

Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this paper, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.

Ground penetrating radar (GPR) is an important shallow electromagnetic non-destructive 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. 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.

Three-dimensional ground penetrating radar (GPR) has gained widespread usage in shallow surface exploration due to its ease of acquisition and ability to capture diverse polarization information Meanwhile, the quantitative interpretation of 3D GPR data through Full Waveform Inversion (FWI), particularly when incorporating various polarization methods, holds substantial practical significance. Nonetheless, Challenges in 3D GPR FWI, including multiple solutions and high memory demands, limit its theoretical potential. In this study, we propose an efficient 3D frequency-domain FWI approach utilizing dual-parameter attributes applied to 3D GPR synthetic polarization data. First, we develop the algorithm, frequency weighting and gradient preconditioning strategies are adopted to reduce the nonlinearity and enhance deep illumination of inversion, and the random source strategy is used to improve the efficiency. Subsequently, we apply the algorithm to investigate different polarization data. Then, we use the algorithm to realize 3D multi-parameters quantitative imaging of GPR polarization data, and discuss the reconstruction characteristics of different polarization data, which makes 3D GPR FWI feasible. Our findings not only enhance the understanding of GPR data but also pave the way for more accurate and efficient subsurface exploration using GPR multi-polarization data.

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.