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 |