The binary segmented volumes were labelled and an intensity radius threshold of 15 to 100 applied followed by a small spot filter to remove round segmented objects with a radius of <50 pixels (134 μm) for visual clarity of the fault plane (Figure 3a). The local aperture at each voxel of the segmented voids was computed from the diameter of the largest ball containing the voxel and entirely inscribed within the object (Hildebrand and Ruegsegger, 1997). Even with the segmentation method described, there is still significant under-sampling of the void population, particularly at the narrower end of the aperture range (Figure 3b). Further work in this area is required and would benefit from machine learning approaches (Andrew, 2018).
We present the data according to a co-ordinate system (\(x,y,z\)) where\(z\) is the vertical axis, which is parallel to the loading direction and corresponds to the direction of axial stress (\(\sigma_{1}\)). The other two (\(x\) and \(y\)) are the horizontal axes, which are perpendicular to the loading direction and correspond to the confining stress (\(\sigma_{2}=\sigma_{3}\)) with their directions arbitrarily assigned but consistent between the two experiments. Void orientations are given in terms of their dip \(\phi\) (deviation from horizontal) and strike \(\theta\) (deviation from \(y\)).