Orientations and fracture sets

By now you have orientation measurements — typed in the field, digitised as planes on the model, or estimated from traces. This page turns that cloud of measurements into fracture sets: the two, three or four preferred orientations that actually describe the outcrop.

Everything downstream keys off sets. One set becomes one fracture set in a DFN, with its own mean orientation, dispersion, size distribution and intensity.

Get them on the stereonet

Open a Stereonet Window from Home → Windows. Your orientations plot as poles.

The first thing to do is contour them, in the Properties panel under Contour Properties. A scatter of poles will always look like it has clusters in it; contouring is what tells you whether they are real.

Method	Use it to ask
Modified Kamb	"Is this cluster real?" Contours are in σ — standard deviations above a uniform distribution — so the contour value answers the question directly.
Schmidt (1%)	"How does this compare?" A fixed 1%-area counting circle contoured in % of data — the classic method, comparable against published nets and against datasets of a different size.

Start with Modified Kamb. Raise Significance (σ) for broader, smoother contours and lower it for tighter, more detailed ones; if a cluster only appears at low σ, be suspicious of it.

Equal area, not equal angle

Density contouring assumes an equal-area (Schmidt) projection, which is the default. If you switch Equal Area off for an equal-angle (Wulff) net, the contours are no longer a valid density estimate.

Splitting into sets

Two routes, and they are complementary rather than competing.

By hand, on the net

The Stereonet ribbon tab has Rectangle, Ellipse, Polygon and Sector selection tools. Draw round a cluster and the poles inside are selected — and because selection is shared across VRGS, they are simultaneously selected in the 3D view and in the trees.

That last part is what makes hand-picking worth doing at least once. Select a cluster on the net, look at the model, and see where those fractures are. If the cluster turns out to be forty measurements off one small face, you have learnt something the algorithm would not have told you. Hide Unselected on the same panel makes this easier.

Having selected a cluster, right-click in the tree → Group to make it a set.

Automatically, by k-means

Select the orientation groups (or the loose measurements) in the tree, then right + click → Auto-cluster Selected Orientations….

• You are asked for k, the number of clusters. The default is 3.
• It needs at least 6 measurements.
• Clustering is on axial pole direction, so a plane and its opposite pole are treated as the same orientation, which is what you want for fractures.
• A new parent group "Classified KMeans N Groups" appears, holding one "Cluster N (M measurements)" group per set. Your originals are not modified — the clusters are copies.

Because it is non-destructive, run it two or three times with different k and compare against the contoured net. k-means will happily return four sets from a population that has two; the contours are what tell you it has overreached.

Use both

Contour first to decide how many sets there are, then auto-cluster with that k to do the sorting. Picking k off the stereonet and letting k-means do the tedious part is faster than either alone, and it keeps the judgement where it belongs.

Reading the set statistics

When you later run Create DFN from Selected Groups, VRGS shows a per-group preview before it commits to anything:

  Cluster 1 (48 measurements): 48 measurements -> dip 82.4, az 137.1, K 24.6
  Cluster 2 (31 measurements): 31 measurements -> dip 79.0, az 044.9, K 11.2

That is the Fisher summary — mean dip, mean azimuth, and K, the concentration parameter. It is worth reading even if you never build a DFN, because it is the most compact honest description of a set you will get.

As a rough reading of K: 1–5 is strongly dispersed, 10–20 is moderate clustering, and 50+ is a tight set. A group needs at least 3 measurements to have statistics computed at all; fewer and it is skipped.

If a cluster comes back down in the dispersed range, go back to the net. Usually it is either two sets that k-means merged into one, or a genuinely diffuse population — and the contours distinguish those.

Getting the numbers out

• Right-click the stereonet → Export writes SVG Document → Stereonet Diagram (or Rose Diagram) at publication quality, or Spreadsheet for the plotted orientations as a table.
• Right-click any interpretation group → Spreadsheet gives a tabular view of every measurement in it, which is usually faster than exporting when the question is just "what were those numbers?".
• For a figure that travels with its statistics, collect the net into an analysis report.

See also

• Stereonet User Guide — the complete property sheet, the rose and dip plots, and the 3D stereosphere.
• DFN: Matching Interpretations — the mathematics behind the Fisher and k-means steps.

Next: measuring fracture intensity.