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.
- Grid
Output Data Object
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- Variance
Output Data Object
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- Points
Input Shapes
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- Attribute
Table field
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- Create Variance Grid
Boolean
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- Target Grid
Choice
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Available choices: user defined, grid system, grid
- Variogram Model
Choice
-
Available choices: Spherical Model, Exponential Model, Gaussian Model, Linear Regression, Exponential Regression, Power Function Regression
- Block Kriging
Boolean
-
- Block Size
Floating point
-
- Logarithmic Transformation
Boolean
-
- Nugget
Floating point
-
- Sill
Floating point
-
- Range
Floating point
-
- Additional Parameters
Node
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- Linear Regression
Floating point
- Parameter B for Linear Regression:
y = Nugget + B * x
- Exponential Regression
Floating point
- Parameter B for Exponential Regression:
y = Nugget * e ^ (B * x)
- Power Function - A
Floating point
- Parameter A for Power Function Regression:
y = A * x ^ B
- Power Function - B
Floating point
- Parameter B for Power Function Regression:
y = A * x ^ B
- Grid Size
Floating point
-
- Fit Extent
Boolean
- Automatically fits the grid to the shapes layers extent.
- X-Extent
Value range
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Minimum: 1.66036175244e-316; Maximum: 1.66031036962e-316
- Y-Extent
Value range
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Minimum: 1.66047558517e-316; Maximum: 1.66043764093e-316
- Grid System
Grid system
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- Grid System
Grid system
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- Grid
Input Grid
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- Variance
Input Grid
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