Iron oxide-copper-gold (IOCG) and iron oxide-apatite (IOA) are two significant mineral deposit types with similar tectonic settings and hydrothermal alteration characteristics. There are huge differences in the geological setting, alteration system, and ore-forming fluid composition among IOCG and IOA deposits, leading to controversial genesis. Distinguishing between these two deposit types is significant to reveal the origin of IOCG and IOA systems. In this study, random forest (RF) was employed to classify IOCG and IOA deposits based on the chemical composition of magnetite measured by the electron probe microanalyzer (EPMA) and laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The obtained results show that (1) a relatively high overall classification accuracy (0.76 for EPMA data and 0.91 for LA-ICP-MS) was obtained via the RF, indicating that the elemental composition of magnetite can effectively distinguish IOCG and IOA deposits; (2) the performance of the RF model based on LA-ICP-MS data is better than that of EMPA data, indicating that the application of more geochemical variables is helpful in distinguishing IOCG and IOA deposits; and (3) the elements V, Mg, and Mn in EPMA data, and Si, Mg, and V in LA-ICP-MS data are identified as the key elements for distinguishing IOCG and IOA deposits.

氧化铁-铜-金 (IOCG) 和氧化铁-磷灰石 (IOA) 是两种重要的矿床类型，具有相似的构造环境和热液蚀变特征。IOCG和IOA矿床在地质背景、蚀变系统和成矿流体成分等方面存在巨大差异，导致成因存在争议。区分这两种矿床类型对于揭示 IOCG 和 IOA 系统的起源具有重要意义。在这项研究中，随机森林 (RF) 被用来根据电子探针微量分析仪 (EPMA) 和激光烧蚀电感耦合等离子体质谱仪 (LA-ICP-MS) 测量的磁铁矿化学成分对 IOCG 和 IOA 沉积物进行分类。所得结果表明(1)总体分类准确率较高(EPMA数据为0.76，EPMA数据为0. 91（LA-ICP-MS）通过RF得到，表明磁铁矿的元素组成可以有效区分IOCG和IOA沉积物；(2)基于LA-ICP-MS数据的RF模型性能优于EMPA数据，说明应用更多的地球化学变量有助于区分IOCG和IOA矿床；(3) EPMA 数据中的元素 V、Mg 和 Mn，以及 LA-ICP-MS 数据中的 Si、Mg 和 V 被确定为区分 IOCG 和 IOA 沉积物的关键元素。