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, non-normal distributions, underlying data trends and complex spatial relationships.

Data Functions
  • arithmetic, log or trig functions of project variables
  • logical functions of characteristics and/or variables
  • function definition / redefinition at any stage

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 time-saving facility for intersecting hole data structures with a 3D geological interpretation to obtain characteristic values for pre-defined downhole intervals.

Statistical Analysis
  • data selection / retrieval by any combination of identity wild-carding, coordinate limits, characteristic lists or variable limits; also available for geostatistics.
  • basic statistics of any variable data subset including mean, sample size, standard error, etc.
  • frequency histogram analysis including normal or log-normal distribution fitting and regression statistics.
  • probability analysis based on normal or log-normal distributions, with regression statistics.
  • correlation analysis with regression statistics.
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 non-normal value distribution. All facilities and results are accessible through an interactive, point-and-click graphics interface.

  • semi-variogram analysis of the spatial variability of project variables, and interactive model-fitting facilities.
  • data transformation capabilities, including log, indicator, rank order and normal scores transforms.
  • anisotropy analysis of spatial variability by application of semi-variogram directional control
  • 3D trend surface analysis of project variables, with regression statistics.
  • semi-variogram models include spherical, gaussian, exponential and power models in any combination.
  • cross-validation analysis for verification of semi-variogram models.

Geostatistical Estimation
  • 3D kriging prediction of variable values to a grid data structure using ordinary or simple kriging techniques and point or volume kriging algorithms.
  • 3D inverse distance interpolation of variables for instances when kriging estimation is inappropriate.
  • geological control of sample and grid cell selection for prediction of geologically influenced variables.
  • directional control of sample selection and prediction algorithms for anisotropic cases.
  • spatial data control of sample selection by search ellipse, octant search and closest N samples.
  • estimation uncertainty - the geostatistical standard error (measure of uncertainty) is stored for variables estimated by kriging techniques.
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 semi-variogram model types, and all necessary facilities for dealing with variable sample density, anisotropy, non-normal 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.
  • manipulation facilities allow the values stored in a grid data structure to be manipulated or combined in functions and stored as new grid variables.
  • manipulation functions include arithmetic, log, trig and logical expressions.
  • data access includes variables, associated geological volumes, and prediction uncertainty.

Spatial Analysis Tools
  • simple volumetrics analyze the volume of any set of complex irregular shapes.
  • intersection volumetrics analyze the volume of intersection between any two sets of irregular shapes.
  • isosurface volumetrics analyze the intersection between a set of irregular shapes and an isosurface (threshold boundary or cut-off) of a grid variable.
  • complex volumetrics analyze the cumulative intersections between any two sets of irregular shapes and a grid variable isosurface.
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, risk-related scenarios for risk assessment and planning purposes. Probability estimation provides the spatial variation of the probability of a variable meeting a pre-defined threshold criterion.
  • sampling control by analysis of the spatial variability of samples and by inspection of the spatial distribution of prediction uncertainty.
  • uncertainty and risk analysis by manipulation of the estimation uncertainty of a grid variable to produce best case / worst case scenarios.
  • probability estimation and spatial analysis of probability by use of indicator transforms.