2-dimentional visualization technique for exploring region of interest in 4-dimensional data

Miyamura, Hiroko  ; Kawamura, Takuma ; Suzuki, Yoshio   ; Idomura, Yasuhiro   ; Takemiya, Hiroshi

In numerical simulations, variations of calculation results with respect to a variable axis are often observed. When the target model is given in 3D, the simulation results become 4D. Such a multi-dimensional dataset given in more than 4D space is analyzed by detailed explorations of regions of interest (ROIs) in multi-dimensional space. However, for high-dimensional and large-scale datasets, this approach requires enormous processing time and effort, and may have difficulty in capturing all the ROIs. Therefore, we propose a technique that is based on a concept of spatiotemporal image. In our technique, a space axis is created by octree, a variable axis is defined in the direction perpendicular to the space axis. Our technique is applied to the results of 3D seismic simulations of a nuclear plant, and regions with characteristic frequency responses of each region are analyzed. Through the analyses, it is demonstrated that our technique can effectively capture ROIs from 4D datasets.