The Assen Fe ore deposit is a banded iron formation (BIF)-hosted orebody, occurring in the Penge Formation of the Transvaal Supergroup, located 50 km northwest of Pretoria in South Africa. Most BIF-hosted Fe ore deposits have experienced post-depositional alteration including supergene enrichment of Fe and low-grade regional metamorphism. Unlike most of the known BIF-hosted Fe ore deposits, high-grade hematite (> 60% Fe) in the Assen Fe ore deposit is located along the lithological contacts with dolerite intrusions. Due to the variability in alteration levels, identifying the lithologies present within the various parts of the Assen Fe ore deposit, specifically within the weathering zone, is often challenging. To address this challenge, machine learning was applied to enable the automatic classification of rock types identified within the Assen Fe ore mine and to predict the in-situ Fe grade. This classification is based on geochemical analyses, as well as petrography and geological mapping. A total of 21 diamond core drill cores were sampled at 1 m intervals, covering all the lithofacies present at Assen mine. These were analyzed for major elements and oxides by means of X-ray fluorescence spectrometry. Numerous machine learning algorithms were trained, tested and cross-validated for automated lithofacies classification and prediction of in-situ Fe grade, namely (a) k-nearest neighbors, (b) elastic-net, (c) support vector machines (SVMs), (d) adaptive boosting, (e) random forest, (f) logistic regression, (g) Naïve Bayes, (h) artificial neural network (ANN) and (i) Gaussian process algorithms. Random forest, SVM and ANN classifiers yield high classification accuracy scores during model training, testing and cross-validation. For in-situ Fe grade prediction, the same algorithms also consistently yielded the best results. The predictability of in-situ Fe grade on a per-lithology basis, combined with the fact that CaO and SiO₂ were the strongest predictors of Fe concentration, support the hypothesis that the process that led to Fe enrichment in the Assen Fe ore deposit is dominated by supergene processes. Moreover, we show that predictive modeling can be used to demonstrate that in this case, the main differentiator between the predictability of Fe concentration between different lithofacies lies in the strength of multivariate elemental associations between Fe and other oxides. Localized high-grade Fe ore along with lithological contacts with dolerite intrusion is indicative of intra-basinal fluid circulation from an already Fe-enriched hematite. These findings have a wider implication on lithofacies classification in weathered rocks and mobility of economic valuable elements such as Fe.

Assen Fe 矿床是一个带状铁地层 (BIF) 的矿体，位于南非比勒陀利亚西北 50 公里处的德兰士瓦超群 Penge 组。大多数以 BIF 为主体的铁矿床都经历了沉积后的改变，包括铁的表生富集和低品位的区域变质作用。与大多数已知的 BIF 型 Fe 矿床不同，Assen Fe 矿床中的高品位赤铁矿（> 60% Fe）位于与辉绿岩侵入体的岩性接触沿线。由于蚀变水平的可变性，识别存在于 Assen Fe 矿床各个部分，特别是风化带内的岩性通常具有挑战性。为了应对这一挑战，机器学习被应用于对 Assen Fe 矿中确定的岩石类型进行自动分类，并预测原位 Fe 品位。这种分类基于地球化学分析以及岩相学和地质测绘。以 1 m 的间隔对总共 21 个金刚石取芯钻芯进行取样，覆盖了 Assen 矿的所有岩相。通过 X 射线荧光光谱法分析这些主要元素和氧化物。对大量机器学习算法进行了训练、测试和交叉验证，用于自动岩相分类和原位 Fe 品位预测，即 (a) k 最近邻，(b) 弹性网，(c) 支持向量机 (SVM) ，（d）自适应增强，（e）随机森林，（f）逻辑回归，（g）朴素贝叶斯，(h) 人工神经网络 (ANN) 和 (i) 高斯过程算法。随机森林、SVM 和 ANN 分类器在模型训练、测试和交叉验证期间产生高分类准确度分数。对于原位 Fe 品位预测，相同的算法也始终产生最佳结果。基于每个岩性的原位 Fe 品位的可预测性，结合 CaO 和 SiO2 的事实₂是铁浓度的最强预测因子，支持了导致阿森铁矿床中铁富集的过程以表生过程为主的假设。此外，我们表明预测模型可用于证明在这种情况下，不同岩相之间 Fe 浓度可预测性之间的主要区别在于 Fe 与其他氧化物之间的多元元素关联的强度。局部高品位铁矿石以及与辉绿岩侵入的岩性接触表明来自已经富铁的赤铁矿的盆地内流体循环。这些发现对风化岩石中的岩相分类和 Fe 等经济有价值元素的流动性具有更广泛的意义。