Physical Sciences and Mathematics Commons^™

Quantification Of Mineral Reactivity Using Machine Learning Interpretation Of Micro-Xrf Data, Julie J. Kim, Florence Ling, Dan A. Plattenberger, Andres F. Clarens, Catherine A. Peters

Environmental Science Faculty Work

Accurate characterizations of mineral reactivity require mapping of spatial heterogeneity, and quantifications of mineral abundances, elemental content, and mineral accessibility. Reactive transport models require such information at the grain-scale to accurately simulate coupled processes of mineral reactions, aqueous solution speciation, and mass transport. In this work, millimeter-scale mineral maps are generated using a neural network approach for 2D mineral mapping based on synchrotron micro x-ray fluorescence (μXRF) data. The approach is called Synchrotron-based Machine learning Approach for RasTer (SMART) mapping, which reads μXRF scans and provides mineral maps of the same size and resolution. The SMART mineral classifier is trained …