Ordinary Kriging for grid interpolation from irregular sample points. This implementation does not use a maximum search radius. The weighting matrix is generated once globally for all points.
| | Name | Type | Identifier | Description | Constraints |
| Input | Points | Shapes (input) | SHAPES | - | - |
| Output | Grid (*) | Data Object (optional output) | GRID | - | - |
| Variance (*) | Data Object (optional output) | VARIANCE | - | - |
| Options | Attribute | Table field | FIELD | - | - |
| Create Variance Grid | Boolean | BVARIANCE | - | Default: 1 |
| Target Grid | Choice | TARGET | - | Available Choices: [0] user defined [1] grid system [2] grid Default: 0 |
| Variogram Model | Choice | MODEL | - | Available Choices: [0] Spherical Model [1] Exponential Model [2] Gaussian Model [3] Linear Regression [4] Exponential Regression [5] Power Function Regression Default: 3 |
| Block Kriging | Boolean | BLOCK | - | Default: 0 |
| Block Size | Floating point | DBLOCK | - | Minimum: 0.000000 Default: 100.000000 |
| Logarithmic Transformation | Boolean | BLOG | - | Default: 0 |
| Nugget | Floating point | NUGGET | - | Minimum: 0.000000 Default: 0.000000 |
| Sill | Floating point | SILL | - | Minimum: 0.000000 Default: 10.000000 |
| Range | Floating point | RANGE | - | Minimum: 0.000000 Default: 100.000000 |
| Linear Regression | Floating point | LIN_B | Parameter B for Linear Regression:
y = Nugget + B * x | Default: 1.000000 |
| Exponential Regression | Floating point | EXP_B | Parameter B for Exponential Regression:
y = Nugget * e ^ (B * x) | Default: 0.100000 |
| Power Function - A | Floating point | POW_A | Parameter A for Power Function Regression:
y = A * x ^ B | Default: 1.000000 |
| Power Function - B | Floating point | POW_B | Parameter B for Power Function Regression:
y = A * x ^ B | Default: 0.500000 |
| (*) optional |