Continuous Microstructural Characterization of a shallow East Antarctic Ice Core Using Machine Learning Super-Resolution and Micro-CT Imaging

Abstract. This study presents a continuous, high-resolution (60 μm) microstructural analysis of a 130 m long ice core B40 drilled in Austral summer 2012/13 at the German Research station Kohnen on the East Antarctic plateau using artificial intelligence (AI)-enhanced micro-computed tomography (micro-CT) imaging and pore network modeling. Antarctic ice cores serve as valuable archives of past climate and environmental conditions. They consist of a sequence of compacted layers with varying density, ice and pore space structure, which are shaped differently depending on the climatic and environmental conditions during deposition and subsequent compaction. The in-depth development of microstructural features provides insights into the densification and pore closure processes as well as climate trends during the formation of the firn column.

However, traditional micro-CT imaging techniques applied to full core samples, although effective, often lack the necessary resolution to capture the intricate microstructure of ice samples fully. To address this limitation, we applied AI-driven super-resolution enhancement methods to improve the clarity and detail of micro-CT images, enabling a more precise quantification of ice core properties. Following AI enhancement, the study performed a comprehensive microstructure analysis on the full firn column, including geometrical, morphological, topological, and transport-related properties. The obtained metrics, including mean intercept length (MIL), cluster size, porosity (density), tortuosity, permeability, etc., can characterize the microstructural evolution of the ice column from snow to bubbly ice across different depths. The results reveal a systematic evolution in pore geometry, connectivity, and transport efficiency with depth, capturing the snow-to-ice transition with high fidelity. Fine and coarse layers were distinguished using the K-means clustering method, and anisotropy was detected in both the ice matrix and the pore space. Principal component analysis (PCA) showed that densification is influenced by multiple factors; in particular, during the pore close-off stage, variations in coordination number and throat dimensions led to distinct densification behaviors among the samples. These findings could contribute to the improvement of densification and air transport models by providing highly accurate data on the microstructure of ice cores and even enabling the definition of new microstructure-related climate proxy parameters.