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Unmixing Mineral Phases, Improving Quantification: Use Machine Learning to Understand Deep-Mantle with STEM-EDS Data

In geochemistry, (scanning) transmission electron microscopy ((S)TEM) has emerged as an advantageous technique for studying minerals and rocks for its excellent spatial resolution (down to subnm). Further, using STEM mode for energy-dispersive X-ray spectroscopy (EDS) mapping enables a robust and quick analysis of the composition of samples. However, several problems currently limit the analytical capability of STEM-EDS. First, the commonly used two-dimensional (2D) EDS scan merely captures an average of the 2D chemical information projected along the probe’s direction, which can be problematic for phase quantification when phases are severely overlapped in the projection. While electron tomography can help retrieve the chemical composition of each phase in this overlapping scenario, it is not feasible for materials which are beam sensitive such as the Earth mantle minerals of interest here. Second, the fact that TEM samples are thin results in low counting statistics for the STEMEDS signal, limiting its sensitivity and reliability when analyzing minor and trace elements. Considering data analysis in general, machine learning (ML) has begun to make an impressive impact in various fields [1]. Here we address the challenges of STEM-EDS quantification by exploiting several ML algorithms, and appropriately applying them to the data analysis. Ultimately, we can automatically identify, segment, and precisely quantify mineral phases in the heavily overlapping scenario. This improved phase quantification further enables us to gain a better understanding of the mineralogy of the deep Earth mantle and conjecture the processes which formed it in the mantle differentiation.