Changes

Shapes of geological surfaces are complex and not readily approximated by simple mathematical functions because they result from a multitude of interacting processes that vary at different spatial scales. Ideally, spatial data should be examined with a spatial sample of regular geometric design. These designs can capture the range of variation exhibited by most spatial phenomena. However, such designs are, for all practical purposes, impossible for most geological work, although in some instances recent developments in satellite imagery allow their economic implementation. In most cases, subsurface geological features are sparsely sampled relative to their complexity and the samples are highly biased to geophysical and/or geological anomalies. Therefore, values of a variable across an area of interest must be estimated by interpolating from a sparse, irregular control point set.

Shapes of geological surfaces are complex and not readily approximated by simple mathematical functions because they result from a multitude of interacting processes that vary at different spatial scales. Ideally, spatial data should be examined with a spatial sample of regular geometric design. These designs can capture the range of variation exhibited by most spatial phenomena. However, such designs are, for all practical purposes, impossible for most geological work, although in some instances recent developments in satellite imagery allow their economic implementation. In most cases, subsurface geological features are sparsely sampled relative to their complexity and the samples are highly biased to geophysical and/or geological anomalies. Therefore, values of a variable across an area of interest must be estimated by interpolating from a sparse, irregular control point set.

Several control point patterns are commonly encountered in geological practice. These include random patterns or clusters ([[file:introduction-to-contouring-geological-data-with-a-computer_fig1.png|Figure 1]]). Geophysical data that contribute to a geological study are gathered in lines. Lines are a special case of clustered points. Each pattern has its own spatial characteristics and must be understood before a meaningful contoured representation can be constructed. Most geological data usually exhibit properties of both end-member patterns. Gridded patterns are rare in geological practice. Most commercial contouring packages compute statistics that when used with visual inspection of the pattern on a base map can greatly aid selection of an appropriate contouring method.

Several control point patterns are commonly encountered in geological practice. These include random patterns or clusters ([[:file:introduction-to-contouring-geological-data-with-a-computer_fig1.png|Figure 1]]). Geophysical data that contribute to a geological study are gathered in lines. Lines are a special case of clustered points. Each pattern has its own spatial characteristics and must be understood before a meaningful contoured representation can be constructed. Most geological data usually exhibit properties of both end-member patterns. Gridded patterns are rare in geological practice. Most commercial contouring packages compute statistics that when used with visual inspection of the pattern on a base map can greatly aid selection of an appropriate contouring method.

==Computer contouring versus hand contouring==

==Computer contouring versus hand contouring==