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. This demand has paved the way for integrating advanced deep learning techniques with conventional methods, offering promising new avenues for cost-effective and rapid geochemical analysis. This study proposes an advanced deep learning-based approach for predicting critical elements-such as arsenic (As), lithium (Li), antimony (Sb), and vanadium (V)-in the Gohar Zamin iron ore mining area in southwest Kerman, Iran. Using X-ray fluorescence (XRF) geochemical data as input, three deep learning models were developed and compared: Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Spatial Attention Networks (SAN). Among the models tested, the CNN demonstrated superior performance in predicting the concentrations of the target elements, achieving the lowest error rates and effectively capturing complex spatial patterns in the geochemical data. The model's ability to extract meaningful relationships from multidimensional data allowed it to outperform both the GRU and SAN models, particularly across low and high concentration ranges. Moreover, the results from CNN-based 3D modeling revealed significant potential for mineral exploration. This research introduces a novel AI-driven framework for utilizing low-cost XRF data in mineral prediction, reducing reliance on expensive analytical techniques while enhancing decision-making in mining operations. The proposed approach offers an efficient and environmentally friendly alternative for geochemical data analysis, contributing to more sustainable mineral exploration practices.Kindly check and confirm the corresponding author of the article and the first/last name of the authors are correctly identified.Checked and confirmed.