The growing availability of time-varying spatial 3D (S4D) data, such as ocean and atmospheric datasets, has created opportunities for studying dynamic phenomena across time and 3D space. However, designing effective visualizations for S4D data remains challenging due to the high cognitive demands and complexity of these datasets. While techniques like animation and small multiples have been applied in Virtual Reality (VR) and desktop environments, the lack of understanding of analysts’ tasks and challenges limits the development of better visualization techniques. To fill this gap, we conducted an empirical study with domain experts across various fields, comparing four visualization techniques: VR animation, VR small multiples, desktop animation, and desktop small multiples. We identified the strengths and weaknesses of the four techniques, as well as key analytical tasks, current practices, and challenges in S4D data analysis. Finally, we outlined future research opportunities for advancing S4D visualization techniques.

Ground penetrating radar (GPR) is a widely used nondestructive testing tool. This letter presents a modified model for quasi-monostatic configured GPRs. In this model, the antenna effects are represented by a set of linear transfer functions and the GPR responses of the layered media are modelled by 3-D Green's functions. In order to obtain sufficient accuracy, the proposed model includes a special effect such that a part of the electromagnetic wave, propagating directly from the transmitting antenna to the receiving antenna, is reflected outside as a new transmitting signal. The experimental results of the full-waveform inversion have demonstrated that considering this special effect in the quasi-monostatic model is effective for improving accuracy. Although both monostatic and quasi-monostatic GPRs can be accurately modelled for full-waveform inversion, the results of the experiment performing inversion of a three-layer media with low permittivity contrast, proved that the latter can achieve better performance due to its intrinsic higher signal-to-noise ratio (SNR).