# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 3 Results
### Roughness Comparators  
The effects of a rough surface on the optical generation and detection of surface acoustic waves can first be demonstrated by examining the results from SRAS measurements from the shot-blast comparator. Figure 4a demonstrates the simple effect of increasing roughness resulting in less light being returned to the detector. It is seen that the average light returned from the sample decreases with increasing roughness up to R\({}_{\mathrm{a}}\) up to \(3.2\,\mathrm{\upmu m}\) and then stabilises around 15% for increasing levels of roughness. The data dropout represents the number of unmeasurable pixels across the surface and is seen to increase with increasing roughness, unlike the light return level, which does not plateau. For this surface type with increasing roughness, there is a decreasing number of positions on the surface that will produce a measurement; however, the level of light returned from the measurable positions will remain roughly constant. Figure 4b plots the standard deviation of the velocity for pixels where the light return is over a chosen threshold against the number of averages used. The first conclusion to draw from this figure is that for a given number of averages, the standard deviation of the velocity increases with increasing roughness. As the number of averages increases, the standard deviation of the velocity decreases; however, it does not drop as quickly as would be expected if the noise was uncorrelated. A 100-fold increase in averages should produce a 10-fold reduction in the standard deviation for random noise, but the drop is around 2-3 times. There are a number of possible reasons for this; for the rougher samples there are two effects, an increase in the acoustic attenuation, leading to a reduction in signal level at the detection location, and secondly, the roughness causes a drop in the return light level, leading to a higher level of noise in the detection channel. Additionally, the data come from different spatial locations on the sample, and although we believe the grain size is very small compared to the measurement resolution, variations in the material could also lead to a plateau in the standard deviation of the velocity with averages. Another source of noise  
is the Q-switch in the generation laser being picked up by the detection electronics, and as this repeats with each generation event. This noise is (partially) coherent and so does not average away as quickly as uncorrelated noise. For the samples where the light return is lower, the effect of the background noise is more marked, and even with a large number of averages, the velocity standard deviation is still quite high. However, reasonable results are still obtained with velocity standard deviation below \(\sim\)50  ms\({}^{-1}\), achievable for all samples; this represents an error on the mean of \(\sim\)2700  ms\({}^{-1}\) of below 2%. Figure 4c plots the mean velocity for \(R_{a}\) between 0.4 \(\upmu\)m and 6.3 \(\upmu\)m; the velocity is seen to decrease from \(\sim\)2715 to \(\sim\)2630  ms\({}^{-1}\). There are two possible hypotheses that may explain this change in wave speed. The first may be related to the defocus in the generation patch, meaning the wavelength of the acoustic wave generated is not consistent. The second possibility is a dispersion of the SAW due to the roughness. The second effect seems more plausible, as for the roughest samples, the focus position was checked. Furthermore, to obtain a change of \(\sim 85\) ms\({}^{-1}\) from defocus would require a 10% change in acoustic wavelength. However, for the case of dispersion due to surface roughness, this is expected to only reduce the velocity of the wave and so is the likely cause of the small shift in mean velocity observed. Finally, Figure 4d summarises these effects by overlaying SRAS velocity maps from each surface onto a photograph of the comparator.