Extraction and parametrization of grain boundary networks in glacier ice, using a dedicated method of automatic image analysis.

Microstructure analysis of polar ice cores is vital to understand the processes controlling the flow of polar ice on the microscale. This paper presents an automatic image processing framework for extraction and parametrization of grain boundary networks from images of the NEEM deep ice core. As cross-section images are acquired using controlled surface sublimation, grain boundaries and air inclusions appear dark, whereas the inside of grains appears grey. The initial segmentation step of the software is to separate possible boundaries of grains and air inclusions from background. A Machine learning approach is utilized to gain automatic, reliable classification, which is required for processing large data sets along deep ice cores. The second step is to compose the perimeter of section profiles of grains by planar sections of the grain surface between triple points. Ultimately, grain areas, grain boundaries and triple junctions of the later are diversely parametrized. High resolution is achieved, so that small grain sizes and local curvatures of grain boundaries can systematically be investigated.