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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.

We estimate the near surface velocity distribution by applying multiscale early arrival waveform inversion (MEWI) to shallow seismic land data. This data set is collected at Wadi Qudaid in western Saudi Arabia with the purpose of characterizing the shallow subsurface for its water storage and reuse potential. To enhance the accuracy of MEWI, we correct for the attenuation effects with an estimated factor Q, and also extract a natural source wavelet from the data. We then applied MEWI to invert the processed data for tomograms on different scales starting from a traveltime tomogram as our initial velocity model. Results suggest that, compared to traveltime tomography, MEWI can generate a more highly resolved velocity tomogram from shallow seismic data by inverting its low‐frequency components on coarse grids and its high‐frequency components on fine grids. The estimated water table in the MEWI tomogram is generally consistent with, but 9% deeper than, the traveltime tomo gram, showing that the water storage in this wadi might be less than expected from the traveltime tomogram. We believe that the more accurate MEWI tomogram will make an economically important difference in assessing the storage potential of this wadi and wadis throughout the world.

The theory of super‐virtual refraction interferometry (SVI) was recently developed to enhance the signal‐to‐noise ratio (SNR) of far‐offset traces in refraction surveys. This enhancement of the SNR is proportional to Nand can be as high as Nif an iterative procedure is used. Here Nis the number of post‐critical shot positions that coincides with the receiver locations. We now demonstrate the SNR enhancement of super‐virtual refraction traces for one engineering‐scale synthetic data and two field seismic data sets. The field data are collected over a normal fault in Saudi Arabia. Results show that both the SNR of the super‐virtual data set and the number of reliable first‐arrival traveltime picks are significantly increased.