Four-dimensional gridding of time-lapse GPR data

Considering the short acquisition time and its high spatial resolution, ground penetrating radar (GPR) can be regarded as an ideal tool for observing subsurface processes. However, only few successful field applications are available in the literature. These studies consider the time dimension only during the interpretation phase. In this study, we present a 4D gridding approach based on a natural-neighbor interpolation of spatially and temporally irregular sampled time-lapse GPR data. We apply our gridding approach on a field data example collected during a meter scale irrigation experiment. To account for spatiotemporal scaling effects, we introduce a scaling factor providing control on the relation between the time and space dimensions. Our result show that by using such a gridding approach on densely sampled time-lapse GPR, we are able to account for subsurface changes that occur during the acquisition time and achieve a generally improved time-lapse processing result.