## 1 Introduction  
Ground Penetrating Radar (GPR) is a high-frequency electromagnetic detection technique that typically operates within a frequency range of 10 MHz to 10 GHz. It is widely utilized in applications such as infrastructure quality assessment, urban road detection, and archaeological exploration due to its high precision, high resolution, and non-destructive nature. The penetration depth and resolution of GPR are strongly affected by operating frequency and site conditions. Higher frequencies (e.g., 500-1000 MHz) provide greater resolution but are limited to shallow depths, while lower frequencies (e.g., 50-200 MHz)enable deeper penetration at the cost of reduced resolution [1; 2; 3]. Detecting shallow geological structures using the GPR technique typically involves focusing on small-scale anomalous features such as voids, cracks, collapses, and reinforcing bars [4; 5]. Obtaining geometric and electrical specifics of these anomalies requires efficiently and intactly separating diffraction from GPR profiles using advanced data processing techniques, since these wave components are the vital responses of small-scale anomalies. This process is considered crucial for both infrastructure quality assessment and near-surface survey using subtle imaging. However, the measured GPR data typically include strong reflections from larger structures as well as weaker diffraction events from small-scale anomalies and noise [3]. Reflections are typically distinguished by horizontal or nearly horizontal linear patterns with strong energy, while diffraction generally exhibit a hyperbolic shape with relatively faint energy, with noise energy usually the faintest [6; 7]. Theoretically, small-scale anomalies are challenging to identify and interpret due to the faint diffraction being usually obscured by crosstalk or even masked by stronger reflection in the GPR profile [8]. Effective methods for separating GPR diffraction are of utmost importance, as they significantly improve the detectability of small-scale anomalies. Diffraction separation was initially introduced in seismic data processing and has now become widely accepted [9; 10; 11]. The most commonly used approaches to separating diffraction are focusing-defocusing, plane wave deconstruction (PWD) enabled dip filtering [12], coherence analysis [8] and multiple singular spectrum analysis (MSSA). The dip filtering method separates diffraction by utilizing the angular differences between diffraction and reflection events based on the extracted dip angle metric of the different components. However, accurately extracting low dip angle information is often considered a tough issue [13]. The focusing-defocusing method aids in diffraction separation by focusing on the amplitudes of signals from discontinuous diffraction points [14; 15; 16]. Likewise, accurately extracting angle-focus information to guarantee the completeness of the diffraction components remains a challenge when there is a reflection-diffraction crosstalk issue involved, particularly in in-site field datasets [17]. The Radon transform methods distinguish diffraction by suppressing reflection through the Radon transform, considering their differences in characteristics within the dip domain [18; 19; 20]. However, the conventional Radon transform often suffers from low-resolution issues due to the residual reflection remained in the separated diffraction profile [18]. The PWD method employs plane wave filters that are designed using the finite difference method to separate diffraction and eliminate reflection [12]. Recent studies have proven the validity of diffraction separation methods like PWD and coherence analysis to process GPR data by exploiting the similarities in propagation characteristics between seismic and radar-emitted electromagnetic waves [8; 21; 22]. However, the performance of diffraction separation methods is significantly affected by scattering artifacts at grid edges caused by improper parameter settings in terms of conventional data processing before separation [9; 23; 24].