Auto-Encoder Neural Network Incorporating X-Ray Fluorescence Fundamental Parameters with Machine Learning
This addresses the need for accurate elemental analysis in mining applications where traditional methods are impractical, though it is incremental as it builds on existing neural network approaches with domain-specific enhancements.
The paper tackles the problem of elemental composition analysis in energy-dispersive X-ray fluorescence (EDXRF) under challenging conditions like moving rocks and limited labeled data, by developing a neural network that incorporates domain knowledge via a forward model; it shows improved performance for low-Z elements (Li, Mg, Al, K) and high-Z elements (Sn, Pb) on a lithium mineral exploration dataset.
We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (like dust). Neural networks do not require instrument and fundamental parameters but training neural networks requires XRF spectra labelled with elemental composition, which is often limited because of its expense. We develop a neural network model that learns from limited labelled data and also benefits from domain knowledge by learning to invert a forward model. The forward model uses transition energies and probabilities of all elements and parameterized distributions to approximate other fundamental and instrument parameters. We evaluate the model and baseline models on a rock dataset from a lithium mineral exploration project. Our model works particularly well for some low-Z elements (Li, Mg, Al, and K) as well as some high-Z elements (Sn and Pb) despite these elements being outside the suitable range for common spectrometers to directly measure, likely owing to the ability of neural networks to learn correlations and non-linear relationships.