Parameters to the SGeMS algorithms are usually input through their graphical interfaces.1Although each algorithm has its own specific interface, all share stan-dard elements, for instance to select a grid, a property, or parametrize a variogram or a distribution. The purpose of this chapter is to describe how to use these recurring graphical elements.
6.1 Algorithm panel
When an algorithm is selected from the algorithms panel, the corresponding parameters graphical interface is displayed (see Fig.2.2).
The algorithm panel, briefly described in Section2.1, is shown in Fig.6.1. The main interface has six parts.
Parameters description
1. Algorithms List of all available algorithms which are grouped into three classes: Estimation, Simulation and Utilities.
2. Parameters input The graphical parameter interface. The parameters for the selected algorithm are entered in this area.
3. Parameters → Load Load parameters previously saved in a file. The param-eters can also be loaded by dragging the parameter file into the graphical parameter interface window.
4. Parameters → Save Save the parameters already entered in the graphical interface to a file. It is recommended that the parameter file has the extension
“.par”, and should be saved outside of the SGeMS project folder.
5. Parameters → Clear All Clear all parameters entered in the current inter-face.
1Chapter10explains how to launch the algorithms without going through the graphical interface
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Figure 6.1 Algorithm panel
6. Run Algorithm Run the selected algorithm with the entered parameters.
6.2 Selecting a grid and property
Selecting a property is done in all algorithm user interfaces for tasks such as, but not limited to, choosing the conditioning data or a training image. It is done through the property selector interface, see Fig.6.2. That selector is linked to a grid chooser:
once a grid is chosen, a list of all the properties belonging to the grid is displayed and the user can select the appropriate property.
6.3 Selecting multiple properties 103
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Figure 6.2 Single property selection widget
Parameters description
1. Grid Name Select an object from the list of all available objects currently loaded in SGeMS. The object can either be a Cartesian grid or a point-set.
Only one object can be selected.
2. Property Name Select a property from the property list of the selected object. Only one property can be highlighted and selected.
6.3 Selecting multiple properties
The selection of multiple properties is done with the multiple property selector, see Fig 6.3, which also allows to order the selected properties. Ordering properties is often necessary, for instance when properties are associated with an ordered set of thresholds or categories. In that case, the first property must relate to the first category, the second property to the second category and so on.
Parameters description
1. Selected Properties The selected properties will appear in this window.
2. Choose Properties Button to select the properties. A selection window will pop up, as shown on the right hand side of Fig. 6.3.
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Figure 6.3 Multiple properties selection interface
3. Available Properties List all the available properties in the current working object. Properties are selected by first highlighting them (multiple properties can be highlighted by pressingCtrlorShift) then clicking the right arrow button, see item 4.
4. Properties selector Use arrows to move properties back and forth between the available property (item 3) and the selected property window (see hereafter item 5). Only highlighted properties are transferred.
5. Selected Properties List of currently selected properties. Those properties can be unselected with the left arrow in item 4.
6. Properties ordering Order the selected properties. The top property is the first one and the bottom property is the last one. To change the order, highlight the property first, then use the up or down arrows to correct the sequence.
6.4 Search neighborhood
Figure6.4shows the search neighborhood interface, which parametrizes an ellip-soid by its maximum, medium and minimum axes. These axes are positioned in space through three angles, see Section2.5.
Parameters description
1. Ranges Maximum, medium and minimum ranges of the search ellipsoid.
2. Angles Rotation angles for anisotropic ellipsoid.
6.5 Variogram
The variogram interface is used in all variogram-based algorithms in SGeMS, see Fig.6.5. This interface allows the specification of variograms with nested struc-tures. Each nested structure is independently parametrized by a variogram type, a contribution, and an anisotropy. Any variogram model built from the variogram interface is guaranteed to be admissible; however, note that the Gaussian model is inconsistent with indicator variables (Armstrong et al.,2003).
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Figure 6.4 Search ellipsoid interface
6.7 Line entry 105
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Figure 6.5 Variogram interface
Parameters description
1. Load existing model Initialize the variogram from a saved variogram file.
2. Nugget effect Value for the nugget effect.
3. Nb of structures Number of nested structures, excluding the nugget effect.
For n structures, the following items 4 to 6 will be repeated n times.
4. Contribution Sill for the current structure.
5. Type Type of variogram for the selected structure (spherical, exponential or Gaussian).
6. Anisotropy Maximum, medium and minimum ranges and rotation angles. In 2D, the dip and rake rotations should be 0 and the minimum range must be less than the medium range.
6.6 Kriging
The selection of the kriging type is done with a special interface. The available kriging types are simple kriging (SK), ordinary kriging (OK), kriging with a trend (KT) and kriging with a local varying mean (LVM). Only OK does not require extra parameters; SK requires a mean, KT requires the components of the polynomial trend (Section3.6.2) and LVM requires the property within which the local means are stored.
6.7 Line entry
The line entry interface is often used either to enter a name, for instance the name of a new property to be created, or to enter a series of values such a thresholds. Note
that any numerical series must be separated by spaces, not commas or semicolons.
The entry is case sensitive.
6.8 Non-parametric distribution
In SGeMS a non-parametric cumulative distribution function, cdf F(z), is deter-mined from a set of threshold values z1 ≤ · · · ≤ zL which can either be read from a file or from a property. F(z) varies by equal increment 1/(L + 1) with:
F(z1) = L+11 and F(zL) = L+1L . The tails of the distribution are obtained by extrapolating to minimum and maximum values, possibly less than z1and greater than zL respectively.
The lower tail extrapolation function provides the shape of the distribution between the minimum zmin and the first threshold z1. The options for the lower tail are as follows. Parameter ω controls the decrease of the function, with the constraint ω ≥ 1.
The greater ω the less likely are low values close to zmin. For ω = 1, all values between zminand z1are equi-probable.
• Z is not bounded: the lower tail is modeled with an exponential function:
F(z) = F(z1) exp)
− (z − z1)2*
∀z < z1. (6.2) The options for the upper tail extrapolation function are similar but applied to the interval (zL,zmax). Parameter ω controls the decrease of the function, with the constraint ω ∈ [0, 1].
The lower the ω value the less likely are extreme values close to zmax. For ω = 1, all values between zL and zmaxare equi-probable.
• Z is not bounded: the upper tail is modeled by an hyperbolic model:
1 − F(z)
6.8 Non-parametric distribution 107 All L − 1 intermediary intervals [zi,zi+1] for i = 1, ..., L − 1 are interpolated linearly, corresponding to a power model with ω = 1.
Note: when zminand zmax values are set to z1 and zL, there is no need for tail extrapolation.
Tie breaking
SGeMS allows to randomly break ties within a non-parametric distribution zi,i = 1, ..., L. Consider the case where n of the L values are identical: zi = zi+1 =
· · · = zi+n, i + n < L. Instead of assigning the same cdf value of F(zi+n)to the n data zi, ...,zi+n, the cdf values F(zi), ...,F(zi+n)are randomly assigned the values i/(L + 1), ..., (i + n)/(L + 1). This is analogous of adding a very small noise to each tie value.
Parameters description
The non-parametric distribution interface is shown in Fig.6.6, and the parameters are described below.
1. Reference distribution Read the reference distribution data either from a data file [ref on file]or from a grid [ref on grid].
2. Break ties [break ties] Randomly break tied values when assigning their corresponding cdf values. There will be as many different cdf values as there are distribution values.
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Figure 6.6 Interface for non-parametric distribution
3. Source for reference distribution If [ref on grid] is selected, the distri-bution values are recorded in a currently loaded SGeMS property. [grid]
and [property] contain the values for the non-parametric distributions. If [ref on file] is selected, the input data file containing the reference distri-bution is entered in [filename]. The reference distribution must be given in one column without header with numbers only.
4. Lower Tail Extrapolation Parametrization of the lower tail. The type of extrapolation function is selected with [LTI function]. If the power model is selected, the minimum value zmin[LTI min]and the parameter ω [LTI omega]
must be specified. Note that the minimum [LTI min] must be less than or equal to the minimum datum value as entered in the reference distribution, and the power ω [LTI omega]must be greater or equal to 1. The exponential model does not require any parameter. No parameters are required when no extrapolation is required.
5. Upper Tail Extrapolation Parametrization of the upper tail. The type of extrapolation function is selected with [UTI function]. If the power model is selected, the maximum value zmax[UTI max]and the parameter ω [UTI omega]
must be specified. Note that the maximum [UTI max]must be greater than or equal to the maximum datum value as entered in the reference distribution, and the power ω [UTI omega]must be less than or equal to 1. The hyperbolic model only requires parameter ω [UTI omega]when the upper tail is unbounded. No parameters are required when no extrapolation is required.
6.9 Errors in parameters
When SGeMS detects erroneous input parameters, it aborts the execution of the algorithm and highlights in red the offending parameters in the algorithm interface.
Leave the mouse pointer over the highlighted fields to get a description of the error, or alternatively, select the question mark cursor ( Help → What’s this orShift-F1) and click on the highlighted fields.