Trend Surface Residuals Gridding

This gridding method is used to illustrate local residuals or differences from regional trends in your data. It does this in two steps:

First, it tries to fit a polynomial trend surface to your data, a 3-dimensional surface represented by a polynomial equation. This is the same process as is done using the Trend Surface Polynomial method.

The second step in computing Trend Surface Residuals is to compare the source data points with the computed trend surface. These "residual" differences are the localized components. The program then grids the residuals using the Inverse-Distance gridding method.

Advantages: The real beauty of determining the residuals from the trend surface is to identify local anomalies from the regional trend. This can identify sample values that stand out from the background.

Disadvantages: This method does not offer meaningful information if there is no regional trend.

Automatic: Choose this option if the program is to pick the best fit polynomial order for your data set.
1st-Order - 6th-Order: If Automatic is not selected, you can choose from one of the listed orders by clicking in its radio button. The "order" of a trend surface equation refers to the highest values of the exponents used in the equation.
- For example, an equation with x and y (to the first power) is a "first order" equation, and creates a flat surface.
- An equation with x2 and y2 is a "second order" equation, and creates a surface with one bending moment.
- An equation with x3 and y3 is a "third order" equation, and creates a surface with two bends.
For a summary of how well each polynomial order fits your data, you can run, separately, a trend surface report.

Back to Grid Method Summary

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