Kriging is a geostatistical gridding method that has proven useful and popular in many fields. This method produces visually appealing maps from irregularly spaced data. Kriging attempts to express trends suggested in your data, so that, for example, high points might be connected along a ridge rather than isolated by bull's-eye type contours.
Kriging is a very flexible gridding method. You can accept the Kriging defaults to produce an accurate grid of your data, or Kriging can be custom-fit to a data set by specifying the appropriate variogram model.
Importing Collar Data (CSV/text format)
When entering data in Drillhole, you must start with collar data. The system creates a unique collar database that you can use to view all drillholes in the project at a glance. You can also plot an initial plan map showing all holes for review purposes. A collar file must exist before survey or assay data can be imported, and before plans and sections can be created.
Overview
This capability was added to Surfer as an integrated data analysis tool. The primary purpose of the variogram modeling subsystem is to assist you in selecting an appropriate variogram model when gridding with the kriging algorithm. Variogram modeling may also be used to quantitatively assess the spatial continuity of data even when the kriging algorithm is not applied.
The Inverse Distance to a Power gridding method is a weighted average interpolator, and can be either an exact or a smoothing interpolator.
With Inverse Distance to a Power, data are weighted during interpolation such that the influence of one point relative to another declines with distance from the grid node. Weighting is assigned to data through the use of a weighting power that controls how the weighting factors drop off as distance from a grid node increases. The greater the weighting power, the less effect points far from the grid node have during interpolation. As the power increases, the grid node value approaches the value of the nearest point. For a smaller power, the weights are more evenly distributed among the neighboring data points.
1. First prepare your data. The data must contains easting (x), northing(y) and Join. The Join is a unique attribute that represents how the points connect to others. Point with the same join will a line. See my data below.
This tutorial shows how gridded surfaces and contour plans are created and used in Discover. A topographic dataset containing spot heights is used as the data source.
An Exercise in Surface Modelling and Analysis Elevation data, stored as a series of spot height points, are located in the table called SPOT HEIGHTS in the \Discover Tutorial \Surfaces folder. The objectives of this tutorial are to interpolate a surface grid and generate a contour plan, create a profile, determine grid slope and aspect, perform sun-shading and clip the grid to a region.