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In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several hundred thousand traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the past few decades, several data attributes have been developed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of the amplitudes, continuity, and geometry in view of the dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). To date, the application of the GST approach has been limited to a few 2D GPR examples. Thus, we take the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply our GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that our set of GST-based attributes can be efficiently computed in three dimensions and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.
Koyan et al. (Tue,) studied this question.