Applied Sciences  
Special Issue on Ground Penetrating Radar: Theory, Methods, and Applications  
[PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2c and [PERSON] 1,2,*c  
Ground penetrating radar (GPR), geophysics exploring technology, could non-destructively acquire high-precision information about the shallow subsurface. GPR has played a crucial role in the detection of urban engineering [1], geological disasters [2], archaeological studies [3], and military affairs [4], with various advantages such as fast speeds, convenience, robust immunity to interference, and light weight [5]. 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. With the further exploration of urban underground spaces, it is difficult for traditional GPR interpretation methodologies to meet the current demand for efficiency and precision. Meanwhile, the diversification of explored objects presents novel challenges to the application scenes of GPR. 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. GPR numerical simulation algorithms are advancing along two primary dimensions: the refinement of modeling precision, and the enhancement of simulation accuracy and efficiency. Various forward modeling algorithms, medium models, mesh generation techniques, and boundary conditions are the hot spots discussed by scholars. In response to the evolving requirements of finely detailed engineering prospecting and the escalating complexity of simulated geometric entities, a concomitant pursuit of the equilibrium between algorithmic computational efficacy and precision has led to the introduction of novel algorithms into GPR forward simulations. These contain methodologies such as the Discontinuous Galerkin finite element method [6], the spectral element method [7], and the symplectic Euler method [8]. Simultaneously, the intrinsic quality of GPR data impacts the accuracy and precision of data interpretation. Given the intricate nature of the explored environment, in which electromagnetic waves are prone to phenomena such as scattering and diffraction during propagation, distinguishing meaningful information from interfering signals within real-time recorded images becomes a formidable challenge. Therefore, the utilization of data processing techniques to suppress noise, extract salient information, and deduce relevant parameters is necessary. Traditional data processing workflows encompass operations such as digital filtering, deconvolution, gain recovery, and time-delay correction. In recent years, the requirements of visualization and real-time capabilities in exploration have propelled inversion imaging into extensive discourse among numerous scholars. Migration is among the topics under consideration, including Kirchhoff integral migration, finite-difference migration, frequency-wavenumber migration, and reverse-time migration [9, 10]. However, conventional migration algorithms predominantly focus on geometric structural imaging, and are difficult to use to describe high-precision reflectivity parameters. In contrast, least-squares reverse-time migration formulates imaging as a least-squares inversion problem [11], demonstrating a robustness to irregular data, elevated imaging precision, and superior amplitude fidelity. Furthermore, full-waveform inversion (FWI) represents a promising tool for attaining high-resolution underground images, directly depicting precise information concerning target material, location, size, and other parameters. FWI concurrently utilizes kinematic and dynamic information, thus serving as a robust instrument for reconstructing intricate geological structures and anomalous bodies.