The groundwater example requires quadrivariate interpolation because the observations are scattered in three-dimensional space and also vary over time. This involves four variables: three spatial dimensions and time.
Conditions: Input values are measured at 3D points over time.; Output is a 4D raster combining 3D space and time.
The groundwater example requires quadrivariate interpolation because the observations are scattered in three-dimensional space and also vary over time. This involves four variables: three spatial dimensions and time.
Conditions: Input values are measured at 3D points over time.; Output is a 4D raster combining 3D space and time.
In the “surface points in time” visualization, the vertical axis represents the distribution in time, not the actual three-dimensional physical location or depth. Each point still has an x, y horizontal location, but the plotted vertical extent encodes elapsed time, as illustrated by lengths corresponding to 250 days and around 500 days.
Conditions: The visualization is the left panel labeled “surface points in time”.; The data represents groundwater measurements over time.
In the “surface points in time” visualization, the vertical axis represents the distribution in time, not the actual three-dimensional physical location or depth. Each point still has an x, y horizontal location, but the plotted vertical extent encodes elapsed time, as illustrated by lengths corresponding to 250 days and around 500 days.
Conditions: The visualization is the left panel labeled “surface points in time”.; The data represents groundwater measurements over time.
Trivariate interpolation differs from bivariate interpolation by operating on values measured at 3D points to produce a 3D raster or volume model, whereas bivariate interpolation uses 2D points to produce a 2D surface. In trivariate interpolation, the sampling may be only partly regular horizontally but scattered vertically, requiring the construction of a continuous volume model from discrete 3D samples.
Conditions: Input values are measured at 3D points.; Output is a 3D raster or volume model.
Trivariate interpolation differs from bivariate interpolation by operating on values measured at 3D points to produce a 3D raster or volume model, whereas bivariate interpolation uses 2D points to produce a 2D surface. In trivariate interpolation, the sampling may be only partly regular horizontally but scattered vertically, requiring the construction of a continuous volume model from discrete 3D samples.
Conditions: Input values are measured at 3D points.; Output is a 3D raster or volume model.