Deep-Learning-Enhanced Electron Microscopy for Earth Material Characterization

Abstract

Rocks, 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 region's location is determined using a Fast Fourier Transform algorithm, followed by iterative refinement via a deformation matrix optimized with Newton's method to minimize image differences. The paired HR and LR images are then used to train a Generative Adversarial Network, where a generator and discriminator jointly train through an adversarial process to produce HR images from LR inputs. This approach accelerates imaging processes, up to a factor of 16, with minimal impact on quality and offers possibilities for real-time super-resolution imaging of unknown microstructures, promising to advance geoscience and material science.