Olivine

Studies of Burgers vector populations are generally focused on materials that are known or expected to have deformed by dislocation creep. The lattice distortion observed in the albite sample is not thought to be a product of dislocation creep (see Chapter 3 for a full discussion of these ideas). Therefore, a crystal with limited and well-known slip systems, olivine, was employed to explore the effects of HR-EBSD on WBV output further. Previous work on this olivine sample has shown that slip systems with [100] and [001] Burgers vectors dominate plastic deformation in the sample (Wallis et al., 2016; Wallis et al., 2017). The histograms of WBV magnitude calculated for the olivine dataset show the same general trend as the albite dataset; i.e. the peak is much narrower in the HR-EBSD processed dataset, due to removal of noise (Fig. 6.8).

174

Figure 6.8 a) Frequency histogram of WBV magnitudes calculated for the conventional EBSD map of olivine. b) Frequency histogram of WBV magnitudes calculated for the HR-EBSD map of olivine. Note almost identical maximum WBV magnitude calculated from each dataset. This differs from the albite histograms presented in Fig 6.3.

In the albite dataset, HR-EBSD processing results in an increase in the largest WBV magnitude by a factor of two. This does not occur in the olivine dataset, with the largest WBVs in the conventional and HR-processed maps measured to be within 7 × 10−6_μm−1

of each other. For an equal comparison of the two maps, threshold values of 0.001 and 0.004 μm−1 _{were chosen (although note there may still be a step size bias), which results}

in the conventional EBSD map being somewhat noisy, although some intracrystalline features can be observed (Fig. 6.9a). Applying threshold values of 0.002 and 0.01 μm−1

to the conventional EBSD data makes the subparallel NE–SW trending lines stand out more clearly (Fig. 6.10a), but none of the more subtle features that can be observed in the HR-processed map (Fig. 6.9b) emerge. In the HR-processed map, sets of structures similar to those presented in Figure 6.5 of Wallis et al. (2017) can be resolved. Their ‘Set 1’, which trends NE–SW is very clear in this map, and less clear but observable is a second set of structures that trends WNW–ESE (Fig. 6.8b). If a lower maximum threshold of 0.002 μm−1 _{is used, the ‘Set 2’ structures become clearer in the map (Fig.}

6.10b). Thus, the bulk information gathered by plotting the WBV magnitude from both datasets is similar, but the HR-processed dataset provides greater fine detail.

175

Figure 6.9 WBV magnitude maps plotted from the a) conventional EBSD and b) HR-EBSD datasets of olivine. The conventional dataset contains lots of noise which obscures some of the fine detail that can be observed in the HR-processed data. Thresholding between 0.001 and 0.014

μm−1 _{was applied to the WBV direction plots (Fig. 6.11). Similar to the albite data, a}

lack of resolution in the conventional EBSD dataset means subtle features are not identified, although a comparison of the two plots shows the main features (Set 1) share the same bulk orientations in each map. Red pixels correspond to the [001] direction in the IPF colour scheme. It is difficult to

Figure 6.10 WBV magnitude maps of the olivine dataset plotted with different threshold values. a) Conventional EBSD data plotted between 0.002 and 0.01 μm−1_{. Much of the noise observed in Fig.}

6.9a is removed, although fine scale features observed in the HR-processed data are still not resolvable. b) HR-EBSD data plotted between 0.001 and 0.002 μm−1_{. The ‘Set 2’ structures are}

176 differentiate structures of any other colour (i.e. direction) from the noise, although in the top part of the map rows of blue pixels associated with the Set 2 structures of Wallis et al. (2017) can be seen (Fig. 6.11a). In the HR-EBSD dataset, Set 2 structures are completely lacking in the WBV direction plots, probably because they are below the minimum magnitude threshold (see future developments below). The Set 1 structures, however, are more sharply resolved, to a detail where the orientation of dominant Burgers vectors in subgrain walls can be seen to vary when different dislocation sets interact (Fig. 6.11b).

Figure 6.11 WBV direction plots of a) conventional and b) HR- EBSD olivine data. Noise in the conventional dataset again obscures detail that can be picked out after HR-processing.See text for further discussion.

Figure 6.12a shows a zoomed region of the olivine dataset (grid ref: [155 212 133 177]) where dominantly green (i.e. the [100] direction) and dominantly yellow (i.e. a set of dislocation structures of mixed character) pixels merge to dominantly red (i.e. the [001] direction). WBV directions can be plotted as arrows on to EBSD maps, which can provide additional information on the geometric compatibility between dislocation structures when they intersect (Fig. 6.12b). Arrows represent Burgers vector directions in sample space (i.e. which way the Burgers vectors point in Cartesian space, with x and y being the map axes and z plotting perpendicular to the map plane), and as they are 3D representations can point in to or out of the map plane, which is not easily visualised in the 2D map plane. 3D representations of the calculated WBV directions can be constructed, as shown in Figure 6.13. The 3D plots are currently also quite difficult to visualise, but it can be seen that the WBVs making up the green line (i.e. [100]) plot into the plane of the map, and the orange-yellow WBV plot out of the plane of the map.

177 Where they intersect, the [001] Burgers vector (red) dominates, and is oriented sub- parallel to the map plane. The WBV directions of the two lines are gradually deflected in to the overlapping zone, resulting in a switch in dominance to the [001] orientation. The 3D structure of the WBV can be more easily visualised in a movie of the 3D plot, as can be seen in M6.1 in the digital appendix at the back of this thesis.

6.5 Discussion

The information gleaned from EBSD can only ever be as detailed as the resolution of the data. It is broadly accepted that the HR-EBSD processing technique increases the angular resolution of EBSD data by two orders of magnitude (Britton et al., 2013; Britton and Hickey, 2018). The analysis presented here shows that if the WBV analytical technique is applied to HR-EBSD data, additional detail can be gathered from EBSD maps that is likely to be unresolvable in conventional EBSD datasets. HR-EBSD removes much of the low-angle noise inherent in EBSD data collection (Fig. 6.1). Because of this, the range of WBV magnitudes calculated per map area is narrower (compare Fig. 6.3a & b, and Fig. 6.8a & b). A smaller range of WBV magnitudes means that a magnitude threshold can be set within closer limits, and is applied to refined data, which, in turn, means more detail can be resolved within WBV magnitude plots (compare Fig. 6.4a & b, and Fig. 6.9a & b).

Figure 6.12 Detail of interacting dislocation sets shown in Fig. 6.11. a) WBV directions plotted using the IPF colour scheme. b) Vector plot of calculated WBVs superposed as white arrows on the IPF map.

In the albite IPF maps, the conventional EBSD dataset shows a wide range of orientations in WBV directions calculated for isolated pixels (Fig. 6.5a). In the HR- processed map, most of the spread has disappeared from the population of isolated pixels, which are dominated by the [010] direction. This switch may be due to HR-

178 processing removing noise that prevents the [010] direction from being resolved in the conventional dataset, but the exact reason for the amplification of the [010] signal remains unclear, and more work is needed in this area to test these effects.

Additional fine detail can be resolved using HR-processed EBSD data in the looping dislocation sets in the albite map. The zoomed region shown in Figure 6.7 shows that there are actually three sets of parallel dislocation structures within what looks like a single structure in the conventional EBSD dataset (or in some areas is not resolved at all). The dislocation sets have alternating [010], [100] and [010] WBV directions, and the map shows that HR-processing can provide detail on how sets of dislocations with different Burgers vectors can contribute to the same intracrystalline features; a level of detail which is not resolvable in the conventional EBSD dataset.