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Previous research highlights the importance of thoroughly characterizing burrow attributes [e.g., burrow percentage (BP), morphology, and diameter] to understand their effects on petrophysical properties. While traditional samples, such as hand specimens and core plugs, and outcrops aid in detailed characterization, their limitations are evident when analyzing large 3-D that can exceed 8 cm in diameter. This study introduces a new approach using ground penetrating radar (GPR) to capture these attributes, a technique not previously utilized in such research. We focus on the Upper Cretaceous Aruma Formation in central Saudi Arabia, characterized by extensive burrow networks with BPs reaching up to 40%. Using a 2.7-GHz antenna over a 1-m2 area, we aimed to assess burrow attributes with GPR, comparing the results to field observations and computed tomography (CT) scans of two samples for a thorough analysis and to evaluate GPR’s effectiveness against field and CT scan methods. This comparison highlighted GPR’s ability to detect complex burrow networks, revealing significant variations in burrow attributes that are not fully captured by other methods. Specifically, GPR data showed that the estimated BP deviated by 5% from CT scans and by 9% from field observations, attributed to its more extensive volume of coverage and deeper penetration. Further analysis confirms GPR’s precision in measuring burrow diameters ranging from 0.45 to 8.5 cm and its effectiveness in providing detailed descriptions of burrow morphology. GPR offers unique insights into large burrow systems, providing data hard to obtain otherwise and enabling a deeper understanding of their characteristics.

A physics-based approach called waveform parsimonious refraction interferometry (WPRI), which interpolates head or refraction waveform, is introduced in this work. WPRI yields a significant advantage in exploration and engineering applications, as it mitigates the excessive time and labor cost in 2-D field acquisitions that require dense receivers and shots while improving coverage in refraction seismic imaging processes. Our proposed method generates the virtual seismic refraction waveform, which involves kinematic and dynamic information with near-perfect accuracy for near-surface seismic waveform inversion and migration. To achieve this, we record data from two shot locations situated at opposite ends of the seismic profile, as well as a handful of near-offset traces along the profile. The virtual head or refraction wavefield is then determined through the convolution and cross correlation of the recorded wavefields with the objective of eliminating common wavepath. Furthermore, we introduce a source wavelet deconvolution step to correct dynamic discrepancies present in the virtual waveform. By implementing this type of technique, we are able to produce virtual seismic data that are highly accurate and can be effectively employed in near-surface seismic imaging applications.