Logarithmic modeling can be helpful for creating solid models of highly anomalous data. If activated, here’s how it works: The control point G-values are converted to natural logarithms. If a control point has a value less than or equal to zero, the logarithmic G-value will be set to zero. A solid model, based on the logarithmic data, is interpolated. The nodes within the model are then converted back to the original units by exponentiation.
- Data sets that contain large "outliers" (i.e. values that are far beyond the typical range of data) are typically problematic when the goal is to highlight these anomalous regions. By computing and gridding the natural logarithm of the control point values, the regional effects of these outliers is more localized as shown by the following diagram. The net effect is to highlight anomalous regions (e.g. contaminant plumes).
- Note: The logarithmic capability should be restricted to data sets that contain geochemical or geophysical data with grossly anomalous data points.
