Abstract

X-ray diffraction analysis (XRD) is an inexpensive method to quantify the relative proportions of mineral phases in a rock or soil sample. However, the analytical software available for XRD requires extensive user input to choose phases to include in the analysis. Consequently, analysis accuracy depends greatly on the experience of the analyst, especially as the number of phases in a sample increases (Raven & Self, 2017; Omotoso, 2006). The purpose of this project is to test whether incorporating machine learning methods into XRD software can improve the accuracy of analyses by assisting in the phase-picking process. In order to provide a large enough sample of X-ray diffraction (XRD) patterns and their known compositions to train the machine learning models, I created a dataset of 1.5 million calculated XRD patterns of realistic mineral mixtures. These synthetic XRD patterns were calculated using crystal structure files from the American Mineralogist Crystal Structure Database (AMCSD) with mineral occurrence data from the Mineral Evolution Database (MED) to mimic geologic knowledge used by expert analysts. Using this dataset, I trained and refined a variety of machine learning models to determine which model is most accurate in identifying the correct mineral phases.

Degree

College and Department

Physical and Mathematical Sciences; Geological Sciences

Rights

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