Deep‐Learning‐Enhanced Electron Microscopy for Earth Material Characterization

AbstractRocks, as Earth materials, contain intricate microstructures that reveal their geological history. These microstructures include grain boundaries, preferred orientation, twinning and porosity, holding critical significance in the realm of the energy transition. As they influence the physical strength, chemical reactivity, and transport and storage properties of rocks, they also directly impact subsurface reservoirs used for geothermal energy, nuclear waste disposal, and hydrogen and carbon dioxide storage. Understanding microstructures and their distribution is therefore essential for ensuring the stability and effectiveness of these subsurface activities. Achieving statistical representativeness often requires the imaging of a substantial quantity of samples at high magnification. To address this challenge, this research introduces a novel image enhancement process for scanning electron microscopy data sets, demonstrating significant resolution improvement through Deep‐Learning‐Enhanced Electron Microscopy (DLE‐EM). This workflow involves capturing high‐resolution (HR) regions within a low‐resolution (LR) area, and registering them with subpixel accuracy. First, the HR reg