Fusion data analysis of imaging data of hydrogen-permeated steel obtained by complementary methods

Chemical imaging, such as mass imaging, provides a distribution image of a particular matter and is crucial for analyzing the chemical and physical mechanisms of a sample. However, methods that provide molecular or elemental distribution do not always have sufficiently high spatial resolution to evaluate the nanosized structures in a sample. To address this issue, a multimodal data analysis method was developed by integrating the obtained low spatial resolution chemical images with complementary methods. In this study, the hydrogen distribution of a steel sample was measured using electron stimulated desorption (ESD) and scanning electron microscopy (SEM). ESD provided the time-course images of hydrogen distribution in the steel sample, whereas SEM provided the outline of the steel sample structure. The multimodal images of the same sample were fused, and then all the data were analyzed together to extract detailed physical and chemical information that cannot be observed by only one of the methods. The alignment of the images obtained using different methods was evaluated based on the minimization of each pixel subtraction. Three different data analysis methods, principal component analysis, least absolute shrinkage and selection operator, and autoencoder, are applied to the image fusion dataset of the ESD image and SEM images to help elucidate the hydrogen permeation behavior through the steel structure.