Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods.

Accurate elemental analysis is a critical requirement for mineral exploration, particularly in regions like Iran, where the mining sector has experienced a substantial increase in exploration activities over the past decade. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) methods have long been regarded as the gold standard due to their high sensitivity and precision; however, their widespread adoption is often limited by high operational costs and complex sample preparation requirements. As Iran's mining industry shifts toward more efficient and sustainable practices-with quantitative studies indicating a significant demand for cost-effective analytical solutions, there is a pressing need for alternative approaches that maintain the analytical strengths of ICP-MS while mitigating its limitations.