Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al₂O_3, Fe₂O_3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.

稀土元素和钇 (REY) 是关键元素和有价值的商品，因为它们的可用性有限，并且在广泛的应用中需求量很大，尤其是在高科技产品中。需求增加和地缘政治压力促使人们寻找 REY 的替代来源，而煤炭、煤炭废料和煤灰被认为是这些关键元素的新来源。这项研究评估了印第安纳州（美国）煤炭的 REY 潜力。然而，尽管煤炭数据揭示了 REY 的潜力，但它受到具有完整 REY 测量的稀疏样本的影响。因此，我们探索机器学习 (ML) 模型和数据增强技术的适用性，以证明它们适用于评估印第安纳州和煤盆地其他地区的 REY 潜力，使用选定的煤炭参数 (Al₂ O _3, Fe ₂ O _3,C、Ash、S、P、Mo、Zn 和 As 含量）作为协变量（指标）。由于印第安纳煤炭数据库中具有完整 REY 数据的样本量相对较小，因此使用了两种数据增强技术（随机过采样示例和合成少数过采样技术）。四种机器学习算法（线性判别分析、支持向量机、随机森林和人工神经网络）被应用于将 REY 潜力建模为分类问题。结果表明，在开发支持向量机 (SVM) 模型之前应用合成少数过采样技术生成了最佳的 REY 分类，准确率为 95%。基于印第安纳州煤炭数据的令人鼓舞的结果可能表明，类似的方法可用于其他煤炭盆地，以筛选具有 REY 潜力的位置。然后可以针对这些位置进行更详细的地球化学调查，以确定最有希望的区域并评估整体 REY 资源。