3D Geostatistics Functionality
Geostatistics is the proven analytical and prediction technology of choice for most geoscience applications. The LYNX implementation of GSLIB 3D geostatistics includes comprehensive functionality for dealing with geological influences, anisotropy, nonnormal distributions, underlying data trends and complex spatial relationships.
Data Functions  

Data functions allow generation of complex variable and characteristic functions that are tailored to project and characterization objectives. 
Data Compositing Tools
Compositing tools provide options for regularization of sample and observation intervals in hole data structures, including the option of honoring geological intersections. Characteristic assignment is a timesaving facility for intersecting hole data structures with a 3D geological interpretation to obtain characteristic values for predefined downhole intervals.
Statistical Analysis  

The comprehensive toolkit of data selection and analysis facilities provides a rapid statistical appreciation of the relationships, distributions and influences present in and between project variables. The flexible data selection facilities may be applied to hole data and/or map data structures. The analytical results provide a logical basis for subsequent geostatistical analysis of spatial variability, particularly with respect to determination of geological influences and selection of appropriate data transforms. 
Geostatistical Analysis  
The geostatistical analysis toolkit is tailored to measurement of the spatial variability of project variables based on available samples, and selection / verification of appropriate prediction techniques and models.The toolkit includes facilities for identifying and isolating geological influences, directional influences due to anisotropy, and underlying data trends. A range of data transformation options extends the analytical capabilities to deal with cases of nonnormal value distribution. All facilities and results are accessible through an interactive, pointandclick graphics interface. 

Geostatistical Estimation  

The vehicle for variable prediction is the 3D grid data structure, which also contains information on grid cell intersections with a 3D volume model representation of geology. These intersections, and their characteristic values, provide the basis for geological control of the prediction process. The prediction capabilities include a range of kriging options and semivariogram model types, and all necessary facilities for dealing with variable sample density, anisotropy, nonnormal distributions and underlying spatial trends. These ensure that the prediction process can deal with cases of complex spatial variability, and allow the process to be appropriately tailored to site conditions in every case. 
3D Grid Manipulation  
3D grid manipulation capabilities provide a means of combining predicted variable values to obtain the variations of complex functions relevant to characterization objectives. Comprehensive grid import / export facilities provide the options of using external prediction facilities where appropriate, and of exporting grid data structures for external analysis. 

Spatial Analysis Tools  

Spatial analysis capabilities are reduced to a generic set of volumetrics tools designed to satisfy all characterization objectives .... from precise volumes of complex geological shapes .... to the volume defined by a variable threshold within a geological unit .... to the geological and variable volumes contained by a complex excavation profile. All spatial analysis results are exportable in standard ASCII file format. Combined with 2D/3D visualization, spatial analysis provides the best possible appreciation of complex subsurface conditions. 
Uncertainty and Risk  
Visualization and spatial analysis of prediction uncertainty provide the means of identifying optimum sample locations. Manipulation of uncertainty with predicted variable values may be used to generate alternative, riskrelated scenarios for risk assessment and planning purposes. Probability estimation provides the spatial variation of the probability of a variable meeting a predefined threshold criterion. 
